(When) Does Third-Party Recognition for Design Excellence Affect Financial Performance in Business-to-Business Markets? 

Third-party recognition for design excellence is often viewed as an event triggering the success of a product with a good design. However, evidence from extant research raises doubt as to whether this form of recognition matters in business-to-business (B2B) markets. Using a demand shock conceptualization to capture the signaling role and demand uncertainty of such recognition, this research argues that third-party recognition for design excellence does create firm value in B2B markets. The effect varies, however, depending on certain types of chief executive officer functional experience (market and finance) and the criteria used by third parties in judging design superiority. Modelfree evidence and matched-sample analysis provides empirical evidence in support of the demand shock perspective and the hypothesized effects. The research adds a new theoretical conceptualization describing the impact of thirdparty recognition for design excellence and unveils several new research areas. Managerially, the results identify several new facilitating conditions that enable B2B firms to create greater value from an innovation strategy based on superior design.

Online Supplement: http://dx.doi.org/10.1509/jm.15.0150

Product design is a critical aspect of the innovation process and involves the configuration of a firm’s product offering (Dahl 2011; Ulrich 2011). Belief in the importance of good design can be found in discussions by chief executive officers (CEOs) who suggest that “good design will become the price of entry” (Brown 2005) and in business press articles stating, “At the Forefront of the Economy, Good Design Is a Priority” (Safian 2013, p. 26). A more nuanced perspective suggests that good design is a “necessary, but not sufficient” (Martin 2011, p. 243) condition for success and “not an unfailing recipe for business success” (Cantamessa 1999, p. 305). Understanding when design creates more or less value for a firm is important because of the significant investments required by a firm to produce a superiorly designed product (Kouvelis and Mukhopadhyay 1995).

Prior research has suggested that third-party recognition for design excellence is a factor influencing the success of products with a superior design (e.g., Chen, Liu, and Zhang 2012). This form of recognition consists of judgments by independent third parties regarding a product’s design superiority. Its impact is considered a result of the recognition’s ability to signal product quality (e.g., Reddy, Swaminathan, and Motley 1998) and the overall innovative capability of a firm (e.g., Xia, Singhal, and Zhang 2016) to customers. Using a signaling theory framework, research has linked the recognition with firm performance measures including long-term sales growth (e.g., Guo 2010; Zhang, Yu, and Xia 2014) and firm value (e.g., Chen, Liu, and Zhang 2012; Xia, Singhal, and Zhang 2016).

While understanding of the topic has benefited from prior research, there has been a tendency to explore the impact of third-party recognition for design excellence in business-toconsumer (B2C) markets such as movies (e.g., Chen, Liu, and Zhang 2012), automobiles (e.g., Balasubramanian, Mathur, and Thakur 2005), and running shoes (e.g., Chen and Xie 2005), where a signaling theory conceptualization has been sufficient to explain the recognition’s impact. In contrast, the small body of research examining third-party recognition in business-tobusiness (B2B) markets has painted a less than favorable picture. Tippins and Kunkel (2006), for instance, examined the impact of Clio awards won by advertising agencies and found limited instances of a positive linkage between design recognition and firm value. Xia, Singhal, and Zhang (2016) recently took a different approach and examined the relative impact of third-party recognition for design excellence across B2B and B2C markets and found that the effect was much larger in B2C markets. These less than positive findings create a difficult situation for B2B marketers who are faced with the question of whether to make the significant financial, market, and technological investments required for creating designs that receive recognition from expert judges (Larsen and Lewis 2007).

The purpose of this research is to provide clear and unambiguous insight for researchers and marketers regarding the implications of third-party recognition for design excellence in B2B markets. The objective is accomplished by addressing several limitations in prior research. First, this research focuses solely on B2B markets. Previously, Xia, Singhal, and Zhang (2016) showed that the impact of third-party recognition is higher in B2C markets. However, their research did not reveal whether the higher effect for B2C firms is relative to no effect for B2B firms or whether the effect in both B2C and B2B markets is significant and the B2C effect is simply larger. Results using event study analysis in our research provide evidence of a positive impact of third-party recognition for design excellence on firm value in B2B markets.

Second, our research addresses a conceptual limitation associated with prior research and its reliance on signaling theory. According to a recent meta-analysis examining switching costs and purchasing behavior in both B2B and B2C markets (Pick and Eisend 2014), unlike in B2C markets, product quality perceptions play a much less significant role in B2B markets. Furthermore, research has suggested that switching costs and purchasing behavior in B2B markets are driven by a host of factors, only some of which are influenced by product quality (Blut et al. 2016). These findings reflect the complex nature of purchasing in B2B markets and cast doubt on the ability of signaling theory and its focus on product quality to explain the impact of third-party recognition for design excellence in B2B markets.

To address this theoretical challenge, our research conceptualizes third-party recognition for design excellence in B2B markets from a demand shock perspective. Theoretically, a demand shock perspective focuses attention on both a buyer’s reaction to third-party recognition for design excellence and a firm’s actions taken in anticipation of how buyers will respond to the recognition (Aggarwal and Wu 2015; Ahmadjian and Oxley 2013; Landsman, Nelson, and Roundtree 2009; Mitchell and Singh 1996; Tokar et al. 2014). Empirical findings based on a matched-sample analysis of 102 instances of third-party recognition for design excellence support the contribution of a demand shock perspective in accounting for the financial impact of this form of recognition in B2B markets.

Conceptualization

Firm value is created when investors update their expectations positively about the level, timing, and riskiness of a firm’s cash flows (Srivastava, Shervani, and Fahey 1998). The primary means by which firms generate cash flow is through sales to existing and potential customers. In B2B markets, an important determinant of existing and new customer purchasing is a buyer’s perception of the switching costs associated with terminating and/or creating an exchange relationship with a supplier (Pick and Eisend 2014). Prior research has identified three types of these switching costs as being influential in B2B markets: ( 1) procedural switching costs associated with finding a new supplier and adopting its product, ( 2) financial switching costs associated with contractual agreements and purchasing benefits such as reward points, and ( 3) relational switching costs associated with brand and personal relationships between a buyer’s and supplier’s brand and employees, respectively (Blut et al. 2016).

The demand shock literature provides a perspective for understanding the impact of third-party recognition for design excellence on B2B switching costs and, ultimately, firm value. A demand shock represents a sudden and unexpected shift in demand due to an exogenous event (Tokar et al. 2014). In line with the definition of a demand shock, third-party recognition for design excellence often occurs suddenly when independent third parties release their judgments to the public; the exact timing of which is often unknown a priori. In addition, the outcome of these judgments is unexpected (or at least uncertain) and exogenous because it is based on the actions of independent judges whose judgments are outside of the firm’s influence.

The literature suggests the demand shock associated with third-party recognition for design excellence in B2B markets occurs at a firm level and as a result of the publicity associated with the recognition. Prior research has studied demand shocks in B2B markets at both industry and firm levels of analysis. Past examples of industry-level demand shocks in B2B markets include the attacks of September 11 (Aggarwal and Wu 2015), changes in economic conditions (Ahmadjian and Oxley 2013), regulatory changes (Landsman, Nelson, and Roundtree 2009), and changes in industry standards (Mitchell and Singh 1996).

Demand shocks also occur in B2B markets at a firm level through the impact of exogenous events on a focal firm. For instance, when Samsung suddenly recalled and stopped production of the Note 7 because of a faulty battery, a demand shock was created for suppliers such as Qualcomm, which provided materials for the manufacture of the Note 7 (Shah 2016). Publicity associated with third-party recognition can also create a demand shock, as described by Sajeel Hussain, chief marketing officer at Cafe´X, a company that sells robotic cafe kiosks to food and beverage retailers. Mr. Hussain describes the impact of third-party recognition on demand for the firm’s products in the following manner: “Do not underestimate the power of industry awards: When Cafe´X was formed in late 2013, nobody knew much about us until we won the Best of Show award at Enterprise Connect 2014. That changed everything for us and we received immediate endorsement from the industry, which gave us the air cover to seek new partnerships and client relationships that otherwise wouldn’t have been possible” (Hussain 2016).

A demand shock perspective also can account for the demand uncertainty that is likely to characterize third-party recognition for design excellence. Demand uncertainty characterizes the impact of third-party recognition for design excellence for two reasons. The first reason relates to the role of product quality in B2B switching costs and purchasing. Procedural switching costs are partially driven by search costs associated with finding a high-quality supplier (Blut et al. 2016). Third-party recognition creates a ranking of firms in an industry that reduces search-related procedural costs associated with switching to a recognized firm by its potential customers. Likewise, the quality signal can positively affect the brand aspect of relational switching costs that are based on brand spillover effects such as those that occur in cobranding strategies (Helm and O¨ zergin 2015), making it less likely that existing customers will switch from a recognized firm. However, while third-party recognition can positively affect quality-related switching costs in favor of a recognized firm, the importance of these switching costs is uncertain; research has suggested that quality perceptions are not necessarily a critical factor affecting purchasing behavior in B2B markets (Pick and Eisend 2014).

A second reason for expecting that demand uncertainty will characterize third-party recognition is that the recognized firm lacks complete information about those switching costs that are unrelated to product quality. For instance, B2B exchange re

lationships tend to be long term, and this creates the opportunity for employees from each firm to develop personal relationships (e.g., Lian and Laing 2007). Breaking these personal bonds creates relational switching costs that are often unknown to those outside of the exchange relationship. Similarly, a recognized firm will have little knowledge of contract-based financial switching costs that are driven by the financial costs of breaking existing buyer–supplier contracts (Blut et al. 2016). Contract length and termination costs can vary widely across buyer–supplier relationships and are often unknown to those outside an exchange relationship because of confidentiality agreements and/or business norms (Hobbs 2016; Snell 2007). The lack of knowledge in these areas creates uncertainty regarding how buyers will react to a firm receiving third-party recognition for design excellence, making it difficult for a recognized firm to gauge the magnitude and timing of thirdparty recognition’s impact on demand in B2B markets.

The demand shock literature recognizes the demand uncertainty that can characterize a demand shock like third-party recognition for design excellence by focusing attention on “bracing” activities firms undertake in trying to anticipate how to respond to the shock. Research has shown that the bracing activity of B2B firms varies in effectiveness. Aggarwal and Wu (2015), for instance, demonstrated that some B2B firms were able to more effectively reshuffle their product portfolio in anticipation of the demand effect 9/11 had on armament purchases by the U.S. federal government. Ineffective bracing activity often occurs as a result of a firm over- or underreacting in response to the demand uncertainty associated with a demand shock. For example, Tokar et al. (2014) reported a tendency of firms in a B2C context to be overly aggressive in building-up excess inventory in response to a demand shock (Tokar et al. 2014). There are reasons to expect that the same mismanagement occurs in B2B markets based on research revealing a tendency of B2B firms to react suboptimally to demand uncertainty by under- or overestimating demand (Dearden, Lilien, and Eunsang 1999).

The preceding discussion suggests that third-party recognition for design excellence in B2B markets can be viewed from a demand shock perspective on the basis of the sudden nature of its occurrence, its potential to quickly affect B2B demand, and the uncertainty associated with its demand impact in B2B markets. In addition to capturing the demand-side aspect of third-party recognition for design excellence, the demand shock perspective also captures the supply-side dimension associated with bracing activity undertaken by firms in anticipation of the demand effect the recognition will cause. It draws attention to the possibility that a firm’s reaction to the recognition can be biased as a result of demand uncertainty characterizing its effect. In the next section, we use this conceptualization to hypothesize the impact of third-party recognition for design excellence on firm value and hypothesize boundary conditions influencing its impact. Figure 1 outlines our hypothesized model.

Several factors provide a basis for expecting third-party recognition for design excellence will result in greater firm value for firms receiving the recognition. One reason relates to the impact of switching costs on customer loyalty (Han and Sung 2008; Lam et al. 2004). Higher customer loyalty protects a firm’s sales and cash flow, and this increases firm value by lowering the riskiness of the firm’s cash flow (Srivastava, Shervani, and Fahey 1998). Third-party recognition should lead to higher customer loyalty because it increases procedural and relational switching costs for existing customers. Procedural switching costs related to search are higher for existing customers of a recognized firm because the recognition makes it more difficult for existing customers to find a supplier with quality superior to that of the recognized firm. Relational switching costs related to brand relationships are also higher for a recognized firm’s existing customers because the recognition increases the possibility of positive brand spillover effects from the recognized firm’s brand to the existing customer’s brand, making it costly for existing customers to break the brand relationship by switching to another supplier. In addition to promoting less risky cash flow through higher customer loyalty, the higher switching costs for existing customers will promote higher cash flow levels in the form of greater share of wallet and cross-buying (Blut et al. 2016).

Third-party recognition should also affect switching costs of potential customers in ways that allow a recognized firm to generate new revenue streams through customer acquisition. For example, the recognition lowers procedural switching costs related to search for a recognized firm’s potential customers because the public nature of the recognition increases awareness by its potential customers of the recognized firm’s product superiority. The recognition also lowers relational switching costs associated with a recognized firm’s potential customers breaking their brand relationships with their current supplier because of the potential positive brand spillover effects that can come as a result of the brands of potential customers being associated with the recognized firm’s brand. The lower switching costs experienced by a recognized firm’s potential customers should promote firm value by supporting customer acquisition and new revenue streams from potential customers (Blut et al. 2016). This could happen even when the product is in a mature category with the award drawing attention to the design excellence of the product and making it salient.

H1: Third-party recognition for design excellence creates value in B2B markets.

The preceding discussion suggests B2B firms will experience an expected positive shift in demand from third-party recognition for design excellence due to its positive impact on the procedural switching costs related to search costs and the relational switching costs related to brand relationships. However, the magnitude and timing of the demand shift can be uncertain because of the influence of additional switching costs about which less is known. For example, there will be uncertainty about relational costs from broken personal bonds and financial costs from prematurely terminated contracts that are unique to exchange relationships. This demand uncertainty could lead to mishandling of the demand shock by the recognized firm through a bracing response that is either too safe or too risky (e.g., Dearden, Lilien, and Eunsang 1999; Tokar et al. 2014). What can prevent mishandling of the sales opportunity presented by third-party recognition for design excellence in B2B markets? Recent research focusing on strategic decision making, in general (Maitland and Sammartino 2015), and demand shocks, specifically (Tokar et al. 2014), has suggested that the availability of task-related information plays a large role in reducing the biasing effects of uncertainty on decision making.

One important form of task-related information is functional experience reflecting the experience accumulated in the performance of an organizational task. We focus on CEO functional experience on the basis of anecdotal evidence suggesting that CEOs recognize the importance of third-party recognition for design excellence and actively respond to the recognition. Bruce Cazenave, CEO of Nautilus, for instance, responded to the Bowflex receiving an award for design excellence by describing the recognition as “an incredible honor and testament to the efforts of our entire product design team” (Business Wire 2014). As such, we expect that CEOs will play an influential role in a firm’s response to third-party recognition for design excellence and that their functional experience will provide insight that can reduce demand uncertainty and allow for more effective bracing activity. The types of CEO functional experiences explored include experience related to markets and finance. These types were chosen because of the insight each type of experience provides relative to B2B switching costs that can create demand uncertainty in the presence of third-party recognition for design excellence.

Chief executive officers vary in their market experience to the extent that they have previously worked in marketing-related and sales-related positions (Hui, Morgan, and Rego 2015). As CEOs gain market-related experience, their knowledge of existing and potential customers’ procedural, financial, and relational switching costs increases. This insight should reduce the demand uncertainty characterizing the recognition and lead to more effective bracing activity. One reason for expecting CEO market experience to be important is that through marketing activity like market research and market segmentation, CEOs acquire general market knowledge related to brands and products (Homburg and Jensen 2007). This information can aid in understanding relational switching costs related to brand relationships. Similarly, sales experience provides insight into the personal nature of exchange relationships, lending insight into the relational switching costs associated with relationships between a buyer’s and supplier’s employees (Johnson, Sohi, and Grewal 2004). In addition, a CEO’s knowledge gained through advertising and related promotional activities provides insight into the search dimension of procedural switching costs (Holak and Reddy 1986).

A CEO’s knowledge gained through past sales efforts to acquire new customers will provide the recognized firm with insight into the financial and relational switching costs for its potential customers. A CEO’s experience in sales activity can also involve working with customers postpurchase to ensure that they experience value from their relationship with a supplier (Burger and Cann 1995). This experience provides a CEO with insight into the adoption aspect of procedural switching costs associated with buyers’ use of a supplier’s product. Thus, the market-related functional experience of a firm’s CEO provides an important information source that can reduce the uncertainty associated with various forms of B2B switching costs, increasing the likelihood that the firm will effectively respond to the demand shock accompanying third-party recognition for design excellence.

H2: A firm creates greater (vs. less) value from third-party recognition for design excellence when its CEO has more (vs. less) functional experience related to markets.

The finance function focuses on effectively managing a firm’s cash flows through financial instruments like equity and debt (Proctor 2014). Experience in finance provides a CEO with various forms of knowledge that provide insight into potential customers’ switching costs in B2B markets. One form of knowledge is contract knowledge gained through participation in financial negotiations with buyers. Business-to-business purchases are often large-volume purchases that involve extended financial negotiations related to payment terms and the provision of trade credit to buyers (Geiger 2017). The knowledge gained through this form of financial experience will provide insight into the financial switching costs associated with terminating contracts. In addition, experience in finance provides a CEO with knowledge related to financial policies, such as inventory cost management, and their value in decision making (Custodio and Metzger 2014). This information can enable CEOs to better understand how past investments can influence procedural switching costs associated with the adoption of a supplier’s product. Chief executive officers with experience in finance will also have a better understanding of switching costs involving personal relationships on the basis of their experience working with clients’ purchase departments as well as the investment community, where personal relationships play an important role (Andrews and Welbourne 2000). These different sources of knowledge a CEO acquires through experience in finance should lessen a firm’s uncertainty regarding the impact of third-party recognition for design excellence on B2B switching costs and allow the firm to best leverage the demand shock.

H3: A firm creates greater (vs. less) value from third-party recognition for design excellence when its CEO has more (vs. less) functional experience in finance.1

The criteria used by third parties when making their judgments of design excellence may include design dimensions related to function, form, or both function and form. These criteria can affect the importance of CEO functional experience by influencing the nature of the demand shock associated with third-party recognition. Specifically, design criteria based on both function and form should dampen the immediate demand shock through a slower purchase deliberation process and less new customer acquisition relative to designs focused on only one design dimension.

One reason for expecting a slower B2B buying decision for designs based on both function and form versus buying decisions for designs based on form or function is related to price. Designs excelling in both function and form will be priced higher because suppliers will need to recoup the higher investments required to excel in both function and form (Kouvelis and Mukhopadhyay 1995; Larsen and Lewis 2007). The higher price will increase the importance of the purchase to buyers because of its greater impact on a buying firm’s financial outlay for supplies, cost structure, and profitability (Wathne, Biong, and Heide 2001). When making more important purchase decisions, firms tend to follow a more formalized buying process that can slow purchase decisions by adding complexity and conflict to the purchase process when the cost of acquired supplies is high (Lau, Goh, and Phua 1999; Pemer and Skjølsvik 2016). In addition, this can also dampen the likelihood of acquiring new customers, whose costs in switching from existing suppliers increase as a result of the aforementioned factors.

A second reason for expecting a slower purchase process for designs emphasizing both function and form involves the financial risk associated with these types of designs. The higher price associated with designs achieving recognition for both function and form creates higher financial risk, which will motivate B2B buyers to consider more purchase criteria (Tullous and Munson 1992). The inclusion of more purchase criteria increases the amount of information search associated with a purchase and this can result in a longer duration for the purchase decision (Weiss and Heide 1993).

A third factor to consider is the complexity associated with adopting designs emphasizing both function and form. Adopting and leveraging products excelling on both design dimensions will likely require buyers to develop more complex processes and systems relative to the processes and systems involved in adopting and leverage designs emphasizing only one design dimension. When faced with more complex purchasing decisions, firms generally create larger buying groups that allow for the input of people from across a firm whose knowledge can aid in decision making (Crow and Lindquist 1985). Identifying whose input should be considered, gathering the input, and using it to make a decision all require time, and this can slow down the purchase process.

Taken together, the higher price, higher financial risk, and greater complexity associated with products excelling on both design dimensions versus one dimension should result in a slower B2B buying decision process than designs excelling on only one design criteria. The slower purchase process will limit the demand shock for products excelling on both design dimension relative to products excelling on one dimension. This will especially be the case with new buyers switching from their existing suppliers to the award-winning firm, thus damping the demand shock effect that occurs through new customer acquisition. As a result, the functional experience of CEOs and the insight it provides for managing the demand shock associated with third-party recognition for design excellence should make less of a difference when design criteria involve both function and form.

H4: The positive impact of CEO functional experience related to markets on the value a firm creates from third-party recognition for design excellence is smaller when the criteria used in determining the recognition are based on both the function and form dimensions of design.

H5: The positive impact of CEO functional experience related to finance on the value a firm creates from third-party recognition for design excellence is smaller when the criteria used in determining the recognition are based on both the function and form dimensions of design.

Several methodological challenges arise when trying to examine the effect of third-party recognition for design excellence. The first challenge involves isolating the effect of third-party recognition, because many activities contribute to a product’s development and the eventual value it creates (Hertenstein, Platt, and Veryzer 2005). To overcome this challenge, we utilized the event study methodology originally developed in the finance literature (Brown and Warner 1980, 1985) and adopted by researchers within the marketing literature (e.g., Sorescu, Shankar, andKushwaha 2007; Wiles, Morgan, and Rego 2012). The event study methodology captures changes in investors’ expectations regarding the future cash flow of a firm experiencing an event. The event study enables us to measure the financial effect of third-party recognition after accounting for all other effects of the firm and the market (Brown and Warner 1985).

The second challenge requires effectively identifying occurrences of recognition for design excellence by a third party. We address this challenge by analyzing awards granted by third parties for product design excellence. Awards for product design excellence are won through a competitive process that firms enter through their own application or by being nominated by another party. The judging of a product’s design excellence involves third-party experts involved in industrial design, marketing, and other areas related to product design. In some cases, customers also play a role in judging the superiority of product designs. Consistent with prior research (e.g., Guo 2010; Xia, Singhal, and Zhang 2016), we measure third-party recognition for product design excellence on the basis of a product winning a product design award.

A third challenge involves testing the causal impact of the recognition and the factors proposed to moderate its effect. To address this challenge, we created a matched sample of “good” design firms not receiving third-party recognition for design excellence using procedures recommended in prior methodological research (e.g., Barber and Lyon 1996; Cram, Karan, and Stuart 2009). In the first step of the matching procedure, we sought potentialmatches within the same industry at a two-digit Standard Industrial Classification (SIC) level. In the second step, we analyzed those firms sharing at least a two-digit SIC match with a firm recognized for design excellence, identifying potential matches as those with a market value within 10% of the recognized firm to find firms that are similar on our dependent variable (i.e., firmvalue). In the third step, we identified the nearest neighbor to the recognized firm based on research and development (R&D)/sales. We used this lastmatching filter to account for factors associated with a firm’s ability and motivation to seek third-party recognition for design excellence. The choice of R&D/sales for matching purposes is based on prior research linking R&D investment with an emphasis on design (e.g., Hsu 2006). Finally, in step four, we verified that no information about the nearest-neighbor match identified in step three was released into the market on the same day the recognized firm received its recognition for its design excellence. This last step ensured that the only difference between the recognized firm and its match was third-party recognition of the former firm for design excellence. The matching procedure employed in the research is consistent with matching procedures used in prior research studying firm value in a variety of contexts related to this research, including marketing management (Mizik 2010) and innovation (Miller 2004).2

We collected the sample used in testing the stated hypotheses by identifying all announcements of firms receiving a product design award that appeared in major daily newspapers and wire services such as the Wall Street Journal, PR Newswire, and Dow Jones Newswire between 1997 and 2014. This resulted in a sample of 369 third-party product design award announcements received by publicly traded firms. Our initial sample consisted only of publicly traded firms to accommodate the event study methodology and the research focus on the value implication of third-party recognition for design excellence. Firms in the initial sample were classified as B2B (vs. B2C) on the basis of their two-digit SIC codes, following prior research examining marketing activity in B2B markets3 (Srinivasan, Lilien, and Sridhar 2011).

Following recommendations from the event study literature (e.g., McWilliams and Siegel 1997), we removed third-party product design award announcements that involved confounding events. For purposes of this study, a confounding event is an event within a three-day window around the focal event that may influence investors’ expectations of firm performance. Examples of confounding events include quarterly performance reports, alliance formations, and executive officer appointments.

One concern is the possibility of self-selection (i.e., whether a firm applied for the award itself). We do not believe a selfselection issue characterizes our sample for several reasons. First, all the awards are based on the evaluation of third-party independent judges, and this prevents firms from being able to directly influence their selection for an award. Second, firms do not completely control whether they are considered for an award. An examination of our sample of awards reveals that third parties consider firms for an award through a nomination process that allows a firm to nominate itself and/or allows a third party (e.g., customer) to nominate the firm. We attempted to isolate those situations in which a firm nominated itself for an award; however, we were unable to identify any instances in which an award recognized whether a firm had self-nominated or been nominated by a third party. Third, we attempted to control for a firm’s motivation to apply for an award by including R&D intensity when constructing a matched sample, recognizing that two firms would be more likely to apply for an award when they place a similar emphasis on R&D. Fourth, our difference model controls for unobserved firm-specific effects that may be related to a firm’s motivation to apply for a design award but are not formally captured in our model.

We also ensured that the recognition for design excellence ( 1) was not the first time investors learned about the existence of a firm’s product and ( 2) was attributed to an independent third party and not to discussions by a competitor. To accomplish this, we searched company annual reports, business databases (ABI Inform), and 10-K filings for all recognized firms in our sample and the products receiving the recognition. We also examined the same documents for all firms in the same fourdigit SIC code to ensure that no competitor discussed the recognition of a sample firm’s design. Our examination of 8,721 10-K filings confirmed that all awards in the sample occurred after the release of product-related information to the public and that there were no competitive discussions of the third-party recognition in the sample.

The final sample consisted of 102 product design award announcements made by publicly traded B2B firms. The sample is similar in size with prior research using the event study method in the marketing literature (e.g., Boyd, Chandy, and Cunha 2010).

Our measure of financial performance is based on changes in the stock price of B2B firms receiving third-party recognition and aligns with research demonstrating the importance that investors assign to demand shocks when valuing a firm (e.g., Humphreys 2010). The event study methodology provides a way to measure the financial effect of third-party recognition for product design excellence, as reflected in a design excellence award, on stock price (Brown and Warner 1985; McWilliams and Siegel 1997). The event study method estimates the abnormal movement in the stock price of a firm receiving thirdparty recognition for design excellence using the Fama–French four-factor model (i.e., the original Fama–French three-factor model with Carhart’s momentum factor) on the day the design excellence award is first announced. This is captured in Model 1 for event k for firm I on day t: ( 1) Rti = ai + bi1Rmt + bi2SMBt + bi3HMLt + bi4UMDt + ARti, where Rit denotes the rate of return (over the risk free rate) of firm I on day t, Rmt denotes the corresponding daily returns on the market portfolio on day t, SMBt (“small minus big”) denotes the difference between portfolios composed of small and big market capitalization at time t, HMLt (“high minus low”) denotes the difference between portfolios composed of high and low book-to-market value stocks at time t, UMDt (“Carhart’s price-momentum factor”) denotes one-year momentum in returns, and ai denotes the intercept term. Rtm captures the usual market factor in stock returns while SMBt and HMLt are meant to reflect risk factors related to size and book-to-market equity. The residual ARit represents a zero-mean abnormal portfolio return that is unexplained by common risk factors. We then use the estimates obtained from the model to calculate abnormal returns (ARs) and cumulative abnormal returns (CARs) by summing the individual ARs across time to account for information leakage before the event date or information dissemination after the event date. We followed prior research in obtaining the ordinary least squares parameter estimates using a 90-trading-day estimation window, ending six days before the focal event (e.g., Wiles, Morgan, and Rego 2012).

Prior research has recommended using a difference model when analyzing matched sample data (e.g., Cram, Karan, and Stuart 2009). As such, we performed the same event study analysis for our matched sample using the event dates for their matched firm recognized for design excellence. We then subtracted the AR for its matched counterpart from the AR for a recognized firm to arrive at our dependent variable. Thus, our main dependent variable is the difference in AR between firms recognized by a third-party for their design excellence and their matched counterparts that did not receive third-party recognition.

We used secondary data to construct the independent variables employed to test the hypotheses. Table 1 provides an overview of the measures and the sources from which we collected the data to measure each variable. Consistent with recommendations from prior research (Cram, Karan, and Stuart 2009), we create difference measures for all independent variables. This involves collecting data for each independent variable for both the firms receiving third-party recognition for design excellence and their matched counterparts. With the exception of design excellence criteria, for which there was no counter measure for the nonrecognized control firms, we subtract the nonrecognized firm measure for an independent variable from that of its corresponding recognized firm in creating the difference measure for each independent variable.

CEO functional experience. We measured the functional experience of CEOs related to markets and finance on the basis of their prior functional experience, following previous research in our measurement process (e.g., Barker and Mueller 2002; Hambrick and Mason 1984). Specifically, we created a set of dummy variables indicating whether a CEO had experience in any of eight functional areas including marketing, sales, engineering, human resources, legal, finance, operations, and R&D. To accomplish the coding, two raters reviewed the biographical background of each CEO for a firm recognized for its design excellence and its matched counterpart to assess the presence or absence of each of the functional areas in a CEO’s background. The coders were first provided with a listing of roles associated with functional experience in marketing, sales, engineering, finance, R&D, operations, legal, and human resources. The roles used to identify a CEO with functional experience related to markets included experience in roles involving marketing, advertising, branding, customer service, merchandising, media planning, sales, selling, business development, and account management. The roles used to identify a CEO with functional experience related to finance included experience roles involving finance, accounting, and bookkeeping and titles such as controller and treasurer.

The coders were then provided with biographies of CEOs for the recognized firms and control firms in the sample. We employed multiple sources in identifying CEO biographies, including firm documents (e.g., 10-Ks), executive profile databases (e.g., Bloomberg’s Executive Profiles and Biographies), and social media databases (e.g., LinkedIn). The coders were provided with a subsample of biographies (5%) and trained on classification of the biographies using the role listings provided to them. This process also identified and resolved questions regarding role-related information in the biographies, interpretation of titles, and assignment of titles using the classification. When there were no more training questions, coders were provided with the full sample of biographies and coded them. There was 98% agreement between the raters, and we resolved any discrepancies. The high level of coder agreement was credited to the extensive training they completed before classifying the full sample.

TABLE: TABLE 1 Measures

Notes: SOA = sales on assets.

We originally wanted to measure actual years for each functional experience. However, data reflecting the actual years of experience for each CEO in each area of work experience were not consistently available across CEOs. As an alternative, we measure each area of functional experience as the percentage of a CEO’s functional experience in that area relative to all areas of functional experience for the CEO. For instance, a CEO with functional experience related to markets, legal, and engineering would have a measure of .33 for market experience, while a CEO with functional experience only in markets would receive a value of 1.0 for market experience. This approach provides a method for determining the intensity of a CEO’s functional experience related to markets and finance versus other functional areas. The value for this measure ranges from 0 (no functional experience in an area) to 1 (all functional experience in an area) and varies between these extremes depending on the other areas of functional experience for a CEO. The way CEO functional experience is measured results in the measures being akin to seasonal dummies. Thus, their effect should be interpreted relative to those functional areas of experience not included in the model (e.g., human, legal).4

Design excellence criteria. The criteria used by third parties when judging design excellence are provided in the recognition. We use this feature of our sample to code the product design awards using a dummy variable indicating whether the stated award is based on both function and form design dimensions ( 1) or is based only on function (0) or only on form (0). The variable was created on basis of the classification of two reviewers independent of the research. The two coders were provided definitions for function and form aspects of design based on definitions offered in prior research (e.g., Homburg, Schwemmle, and Kuehnl 2015). The definition provided to the coders for design function stated that function refers to the ability of a product to successfully perform a task and deliver on customer expectations in performing the task. The coders were also provided with examples of ways to describe design function that included descriptions of the product being “helpful,” “capable,” “sturdy,” and/or “practical” in its performance of a task. The definition provided to the coders for design form stated that form refers to the “look” or aesthetic appeal of the product and its features, including size (e.g., big, small), shape (e.g., round, wide) and packaging (e.g., creative, eye-catching). The coders were also provided with examples of ways to describe design form that included descriptions of the product as being “beautiful,” “appealing,” “elegant,” and “striking” in its appearance. We collected the design criteria for each award from information provided in each announcement and on the third party’s website. We provided a subsample (5%) from the full sample to the coders to train them on use of the criteria in classifying each award. Afterward, the coders were given the full sample to code. There was 82% agreement between the coders, and we resolved the remaining discrepancies.

Factors other than the variables identified in the hypotheses may have an impact on investors’ reactions to a firm receiving thirdparty recognition for design excellence. At a firm level, the efficient market hypothesis suggests that changes in firm stock price are the result of investors reacting to new information about a firm. Given our focus on product design excellence, we accounted for the newness of design-related information by controlling for the number of prior design awards received by a firm for other products before the focal award examined in our analysis. We also recorded product age to account for the level of prior information investors had about a recognized product. We control for marketing by including a measure of promotional intensity based on a firm’s investment in sales, general, and administrative (SG&A) relative to its revenue. We account for the potential impact of prior performance on investor reaction to a superior product design by including a variable measuring sales on assets (sales/total assets). The model also controls for the influence of CEOs by measuring whether a CEO served as both CEO and chair of the board.

The research also controls for macroenvironmental factors. We control for environmental uncertainty and environmental munificence by using measures from prior research based on an analysis of industry sales (e.g., Boyd, Chandy, and Cunha 2010). We measure environmental uncertainty by regressing industry sales onto a five-year time period prior to a firm announcing its participation a trade show. The standard error from this regression is then standardized by the mean of industry sales over the fiveyear time period. Environmental munificence is measured using the same regression analysis of five-year industry sales and dividing the coefficient from the analysis by the mean of industry sales over the five-year time period. We account for the competitive nature of a recognized firm’s industry by including a measure of industry concentration using the Herfindahl index.

Several aspects of a product design award may influence investor reaction to a firm winning an award. The judging varies in line with the identity of the third-party expert, which could lead to different performance impacts of an award (Gemser, Leenders, and Wijnberg 2008). All awards involved evaluations by industry experts, but there were awards that also included feedback from consumers. Drawing on prior research suggesting the influence of consumer word of mouth (Chakravarty, Liu, and Mazumdar 2010), we included a dummy variable indicating whether the judging included consumer feedback. The research also controls for the sponsor of the award by identifying those awards sponsored by a magazine to account for market exposure to the recognition.

The last set of control variables account for possible concerns regarding the validity of our matching process. Cram, Karan, and Stuart (2009) empirically demonstrate that validity in matched-sample analysis is best established by recognizing the inexactness inherent in matching techniques and the potential of unobservable factors not accounted for by matching. Following their recommendation, we include difference measures for the variables used in creating the matched sample: industry SIC, market value, and R&D intensity. Difference in industry SIC code was measured using a dummy where the value of 1 reflected an exact match at a four-digit level between a recognized firm in our sample and its matched counterpart; the measure was given a value of 0 otherwise. Differences in market value and R&D intensity were measured between a firm recognized for design excellence and its matched counterpart.

TABLE: TABLE 2 Correlations and Descriptive Statistics

*p < .05.

**p < .01.

Table 2 provides the correlation matrix and descriptive statistics for the independent variables and control variables. We used the following model specification to test our hypotheses: ( 2) DiffAbnormalReturni, I = b0 + b1DiffCEOMarketExperi, m + b2DiffCEOFinanceExperi, m + b3DiffCEOMarketExperi, m · DesignExcCriteriai + b4DiffCEOFinanceExperi, m · DesignExcCriteriai + b5DesignExcCriteriai + b6DiffPriorAwardsi, m + b7ProductAgei + b8DiffPromotionalIntensityi, m + b9DiffFirmSOAi, m + b10EnvUnci, m + b11EnvMuni, m + b12CompetIntensityi, m + b13ConsumerJudgei + b14MagSponsori, m + b15CEODualityi + b16DiffSIC4Digiti, m + b17DiffMarketValuei, m + b18DiffR&Di, m + b19YearDummiesi, k + e, where

DiffAbnormalReturni, m = difference in AR between third-partyrecognized firm I and its matched counterpart m,

DiffCEOMarketExperi, m = difference in CEO market experience between third-party-recognized firm I and its matched counterpart m,

DiffCEOFinancialExperi, m = difference in CEO financial experience between third-party-recognized firm I and its matched counterpart m,

DesignExcCriteriai = third-party recognition received by firm I based on both function and form design criteria,

DiffPriorAwardsi, m = difference in prior design awards between third-party-recognized firm I and its matched counterpart m, ProductAgei = days since launch of product recognized by third party for design excellence for firm i,

DiffPromotionalIntensityi, m = difference in SG&A/revenue between third-party-recognized firm I and its matched counterpart m,

DiffFirmSOAi, m = difference in sales on assets between thirdpartyrecognized firm I and its matched counterpart m, EnvUnci, m = environmental uncertainty for industry of thirdparty recognized firm I and its matched counterpart m,

DiffEnvMuni, m = environmental uncertainty for industry of third-party-recognized firm I and its matched counterpart m,

DiffCompetIntensityi, m = firm concentration for industry of third-party-recognized firm I and its matched counterpart m,

ConsumerJudgei = consumer contribution to the third-party recognition received by firm i,

MagSponsori = magazine-sponsored third-party recognition received by firm i,

CEODualityi = CEO is also chair of the board for firm I recognized by third-party for design excellence,

DiffSIC4Digiti, m = difference in SIC four-digit code between third-party-recognized firm I and its matched counterpart m,

DiffMarketValuei, m = difference in market value between thirdpartyrecognized firm I and its matched counterpart m,

DiffR&Di, m = difference in R&D intensity between third-partyrecognized firm I and its matched counterpart m, and

YearDummiesi, k = vector of year k in which firm I received third-party recognition for design excellence.

Model-free evidence. For the first step of our analysis, we aimed to provide evidence of a demand shock being associated with third-party recognition for design excellence. We use the sample of recognized firms and compare their sales growth with that of their matched counterparts during each of the four quarters immediately following recognition. The benchmark for generating sales growth during each quarter subsequent to recognition is the quarter immediately preceding recognition. Table 3 reveals that only recognized firms experience any significant postrecognition sales growth, and this growth occurs only during the quarter immediately following their winning of an award. Nonrecognized firms do not experience any significant sales growth during the same period. It is important to note that there is a large range of values characterizing sales growth for recognized firms, suggesting that there is significant variation in the extent to which firms are able to generate more or less sales from the recognition. Furthermore, 56% of the sample experienced a positive return, while 44% of the sample experienced a negative return. This distribution aligns with our demand shock perspective. These results provide empirical evidence consistent with a demand shift for firms receiving third-party recognition for design excellence and provide support for a demand shock conceptualization.

TABLE: TABLE 3 Demand Shock Model-Free Evidence

As Table 4 shows, third-party recognition for design excellence positively affects firm value on the day it is announced. The AR during the event day window (0) is positive and significant (CAR = .78, p < .05). The lack of significance for any of the alternative ARs and CARs suggests that there is very little leakage of the recognition before its announcement, which is consistent with the sudden nature of the information being made publicly available and aligns with our demand shock conceptualization. These results provide support for H1.

TABLE: TABLE 4 ARs and Third-Party Recognition for Design Excellence

TABLE: TABLE 5 Firm Value and Third-Party Recognition for Design Excellence

*p < .10.

**p < .05.

***p < .01.

Note: Yearly indicator variables included in model but not reported.

To test H2–H5, we use the difference in ARs between the firm recognized for design excellence by a third party and its matched counterpart on the day of the recognition announcement the dependent variable. Table 5 provides the model statistics and the results from regressing the ARs from the event day onto the hypothesized variables and control variables included in the model. We also calculated the variance inflation factors for the variables in our model and found all variance inflation factors to be well below the recommended threshold of 10. The full model is significant and explains 31% of the variation in the abnormal stock return associated with a firm receiving third-party recognition for design excellence.

H2 focused on the impact of CEO market experience. The sign for the CEO market experience coefficient is positive (coefficient = .08) and significant (p < .05). As a result, there is evidence in support of the importance of CEO market experience as a factor influencing the value a firm creates from receiving third-party recognition for design excellence.

H3 examined the effect of CEO experience related to finance. The positive (coefficient = .09) and significant (p < .01) coefficient for finance experience provides support for H3. This finding illustrates the important contribution of CEO finance experience as a factor influencing investors’ expectations regarding the value creation opportunity provided by recognition for product design excellence.

H4 and H5 argued that the positive effect of CEO market and finance experience is smaller when third-party recognition for design excellence is based on criteria that include both functional and form dimensions of design because of the slower purchase process associated with products emphasizing both design dimensions and the resulting dampened demand shock. The results support both H4 and H5 based on the negative and significant coefficient for the interaction term involving CEO market experience and design excellence criteria (coefficient = -.08; p < .10) and the negative and significant coefficient for the interaction term involving CEO finance experience and design excellence criteria (coefficient = -.08; p < .05).

Overall, the results provide strong support for viewing third-party recognition for design excellence as a factor creating firm value in B2B markets. The results also support the influence of a CEO’s functional experience related to markets and finance on the value created from third-party recognition for design excellence in B2B markets. The significant impact of CEO functional experience highlights the importance investors assign to a firm’s anticipated reaction to recognition when evaluating the financial impact of third-party recognition for design excellence. In addition, the significant and negative interaction of functional experience with design excellence criteria provides empirical evidence demonstrating that management’s contribution depends on the level of sales opportunity created by third-party recognition for design excellence.

We performed several forms of follow-up analysis to assess the robustness of the research findings. The first follow-up analysis involved testing the proposed model with alternative dependent variables. The original analysis used the difference in AR between each firm recognized by a third-party for design excellence and its matched counterpart as the dependent variable. This difference measure represents a novel approach to using ARs from an event study within marketing. To address concerns that our results are an artifact of the way we constructed the dependent variable, we created an alternative difference measure using one-day buy and hold returns on the event day for the recognized firms in our sample and their matched counterparts. The results from testing this alternative model are consistent with the original results (see Web Appendix B). As an additional robustness check related to our dependent variable, we also created a random sample of ARs for our recognized firms and their matched counterparts by randomly identifying a date within 60 days of the actual award event date. We calculated ARs for the random event date and the analysis rerun. None of the hypothesized findings from our original model replicate with the random sample of ARs (see Web Appendix C).

We also aimed to ensure that findings related to CEO functional experience, in general, are not a by-product of the measure used for CEO functional experience by examining an alternative measure of the variables. As highlighted previously when discussing the measure of CEO functional experience, we originally sought to measure actual years for each functional experience. However, this was not possible because of missing data for multiple CEOs with respect to time spent in a particular function. Instead, the analysis measured each area of functional experience as the percentage of a CEO’s functional experience in that area relative to all areas of his or her functional experience. The robustness analysis aimed to validate that it is the intensity of functional experience and not simply the presence of experience that is important. We pursued this objective by creating a dummy variable equaling 1 if a CEO has experience in a functional area and 0 otherwise. The alternative approach led us to recode CEO market and finance functional experience, respectively, resulting in a binary indicator measure for each type of experience. None of the CEO functional variables are significant using this new indicator measure, confirming that it is the intensity of CEO functional experience that matters to investors (see Web Appendix D).

We also explored whether the results from our analysis are sensitive to the presence of outliers. To do this, we Winsorized the data at 99% and reran the analysis. We were also sensitive to the possibility that our choice of a cutoff value for Winsorizing the data could potentially bias the results. Therefore, we also Winsorized the data at 95% and reran the analysis. The results are robust across both levels of Winsorization, suggesting that outliers are not influencing the results (see Web Appendix E).

With this research, we aimed to provide insight into the financial impact of third-party recognition for design excellence in B2B markets. To do so, we employed a demand shock conceptualization to account for possible reactions to the recognition. The research hypothesized a positive average effect across B2B markets and hypothesized CEO functional experience and the design criteria used by experts when determining design excellence as moderating factors. An event study method of analysis involving a matched sample and difference score regression analysis examining 102 incidences of design excellence awards received by B2B firms both provide strong support for the hypothesized model. Next, we discuss the theoretical and managerial implications of the research findings.

One contribution of this article is the clarity and confidence it provides to the small body of research on third-party recognition for design excellence in B2B markets. Prior research on the topic created doubts regarding the effect of this form of recognition in B2B markets because of limited positive findings (e.g., Tippins and Kunkel 2006) and findings that showed lesser effects in B2B versus B2C markets (e.g., Xia, Singhal, and Zhang 2016). Our research addressed these empirical concerns by analyzing a cross-sectional sample of third-party recognitions for design excellence received by B2B firms. The positive results from the event study method of analysis and the model-free evidence regarding an immediate and significant sales effect eliminate doubts about the significance of this type of recognition in B2B markets, highlighting the importance of third-party recognition for design excellence for B2B researchers and managers.

A second theoretical contribution of the research is the broader conceptual perspective it provides regarding thirdparty recognition for design excellence. To this point, researchers have heavily emphasized the impact of third-party recognition using signaling theory and focusing on changes in customer demand as a result of changes in consumers’ perceptions of a firm’s product quality (e.g., Balasubramanian, Mathur, and Thakur 2005; Chen, Liu, and Zhang 2012; Chen and Xie 2005). The current research offers an alternative but complementary view by focusing on the topic from a demand shock perspective. This new perspective recognizes the demand-side impact suggested by signaling theory while highlighting a supply-side perspective that emphasizes a firm’s bracing activity in the context of demand uncertainty as it attempts to leverage the demand shock. This latter managerial viewpoint is missing from a signaling theory perspective, but the empirical findings involving CEO functional experience support its importance and the contribution of a demand shock conceptualization.

Furthermore, the results related to CEO functional experience cannot be explained purely by signaling theory. Prior research has suggested that investors should expect superior designs from CEOs with market experience because of the important contribution of this type of knowledge in creating superior product designs (Im et al. 2016). Thus, CEO market experience should be a weak quality signal. Alternatively, investors should have lower expectations of superior designs based on CEO finance experience because of the internal focus of this type of experience. This should make CEO finance experience a stronger signal to investors relative to CEO marketing experience. However, follow-up analysis of the standardized coefficients reveals that the effect of CEO market experience (beta coefficient = .62) and the effect of CEO finance experience (beta coefficient = .65) are very close in magnitude. The closeness of the effects aligns with the demand shock arguments we proffer here, such that both types of experience provide important information related to B2B switching costs and their role in determining the effect of third-party recognition for design excellence.

A third contribution of the research involves its addition of hitherto unexplored boundary conditions to the literature. Prior research by Balasubramanian, Mathur, and Thakur (2005) has found that the effect of recognition for design excellence can vary by the source of recognition. Guo (2010) showed how the impact can vary on the basis of differences in the history of design’s competitive importance across industries. In our research, we included several controls to account for these findings. The significance found for CEO role experience related to markets and finance demonstrates the importance of adding CEO functional experience to the set of factors known to create boundary conditions moderating the impact of third-party recognition for design excellence.

While these findings broaden researchers’ theoretical understanding of third-party recognition and design excellence in B2B markets, they also raise several important questions in need of research. For instance, do investors vary the importance they assign to CEO functional experience in managing a demand shock depending on the source (e.g., economic events, natural disasters), valence (positive or negative), and level (macro or micro) of a demand shock? Moreover, what other factors create demand shocks in B2B markets besides third-party recognition for design excellence? Research in B2C markets has suggested that firm promotional activity can create a demand shock (e.g., Pauwels et al. 2004). Does firm-level promotional activity have a similar effect in B2B markets? If so, how does the effect vary across different forms of promotion in B2B markets? Addressing these questions will be important for marketing researchers to fully understand the role of demand shocks and the factors that influence a firm’s ability to manage them effectively.

Our research suggests several factors managers should consider to ensure that their firms benefit from innovation strategies based on superior designs. First, the findings recommend that B2B marketers invest in receiving thirdparty recognition for design excellence. The doubts induced by prior research regarding the effect of this kind of recognition in B2B markets created a difficult situation for B2B marketers. It forced them to choose between making significant investments to achieve recognition (Larsen and Lewis 2007)—without any confidence that the investments would pay off—and forgoing the investments and assuming the risk of being classified into a low-quality group because of a lack of recognition (Rossman and Schilke 2016). This research provides strong evidence in support of the former decision, and B2B marketers can use this empirical evidence to acquire the resources necessary to achieve third-party recognition for design excellence.

Another implication of the research is that it calls for marketing managers to alter their view regarding third-party recognition for design excellence. A recent survey of practitioners reveals that managers see little direct economic value in third-party recognition for design excellence (Sung, Nam, and Chung 2010). This research clearly shows that this form of recognition warrants more serious attention from managers because of the positive impact it can have on firm value and sales, as shown in the event study and model-free analysis, respectively. Importantly, the research also reveals that B2B marketers can expect to best leverage the recognition when their CEO possesses the right types of functional experience. We found that marketing and finance experience were especially helpful in managing and benefiting from the demand shock accompanying third-party recognition for design excellence.

Finally, promotion intensity has a significant and positive effect on the difference in ARs. When viewed in light of the overall positive effect we found for third-party recognition of design excellence, this finding suggests that firms can amplify the positive effect of achieving third-party recognition for design excellence through their marketing efforts. Importantly, we measured promotional activity based on expenditures toward SG&A relative to revenue drawing on prior research suggesting that the sales force is the primary promotional activity within B2B firms (Biemans, Brencic, and Malshe 2010) and that sales force activity is typically associated with short-term promotional activity that aligns closely with the short-term nature of a demand shock (Homburg and Jensen 2007). The positive effect found for promotion intensity suggests that investment in the sales force can be one way in which a B2B firm can enhance the returns from third-party recognition for design excellence.

There are several limitations to note when considering the research findings. Because of data limitations, our measurement of CEO functional experience did not measure actual years of experience. Instead, CEO functional experience was measured on the basis of the relative intensity of the CEO’s experience in different functional areas. This measurement approach should be kept in mind when interpreting the results.

Another point to consider is that the removal of announcements with confounding events in the research helped ensure internal validity of the empirical findings. However, the trade-off is that it may weaken external validity if the recognition is considerably more significant when conflated with other events (Warren and Sorescu 2017). Thus, one should interpret the results reported in this research as a conservative estimate of the impact of third-party recognition for design excellence on firm value in B2B markets.

Finally, the research did not identify the path through which CEO functional experience has its influence within a recognized firm. The CEO effect could be the result of the CEO’s individual decision making or it could be the result of the CEO’s effectiveness in coaching the firm’s staff (or some combination thereof). Future research is needed to provide insight on this issue.

1 In testing H2 and H3, the baseline is other CEO functional experiences because each type of CEO functional experience is measured as a percentage of a CEO’s total experience across functional areas. We describe the measures in more detail subsequently in the article, when discussing the measures employed in the research.

2 We conduct various robustness tests (discussed in a subsequent section) to ensure that the matching process does not bias the analysis.

3 A table providing the SIC codes used in the analysis at a fourdigit level appears in Web Appendix A.

4 We thank an anonymous reviewer for highlighting this feature of the measures employed in the research for capturing CEO functional experience.

DIAGRAM: FIGURE 1 Third-Party Recognition for Design Excellence and Its Impact on Firm Value in B2B Markets: A Demand Shock Perspective

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A Cinderella Story: How Past Identity Salience Boosts Demand for Repurposed Products 

Like Cinderella, many repurposed products involve a biographical transformation, from a tattered past identity (e.g., an old airbag) to a product with a valuable but different new identity (e.g., a backpack made from an airbag). In this article, the authors argue that marketers should help customers infer such product stories by highlighting the products' tattered past identities. Three field experiments and four controlled experiments show that making a product's past identity salient boosts demand across a variety of repurposed products. This is because past identity salience induces narrative thoughts about these products' biographies, which in turn allows customers to feel special. Results also suggest that this strategy of past identity salience needs to be particularly well-crafted for products with easily discernible past identities. These findings highlight a promising new facet of storytelling (i.e., stories that customers self-infer in response to minimal marketer input); create new opportunities for promoting products with a prior life; and deliver detailed guidance for the largely unexplored, growing market for upcycled and recycled products.

Keywords: storytelling; narrative thinking; repurposed products; upcycling; recycling; felt specialness; product history

A worn-out airbag becomes a backpack. A leaky boat turns into a table. An old mosquito net transforms into a laptop sleeve. Product biographies that entail transformations from an old past identity to a new product identity are characteristic of production modes that involve the repurposing of old or dysfunctional products into new products. These include recycling, which is already common practice ([86]), and upcycling, which is gaining in popularity ([63]; [85]). For instance, a search on Instagram for upcycling yields over 1 million results, and the upcycled offers on the online marketplace Etsy have increased by 1,000% since 2011, with Etsy now featuring more than 300,000 different upcycled products in the United States alone. Many established companies, such as the outdoor brand Patagonia or the fashion retailers ASOS and Urban Outfitters, have also started to operate in this domain.

However, when companies offer new products manufactured from old or waste products, how can they ensure that customers will demand these products? In this article, we show how the unique properties of repurposed and transformed products illustrate a novel way of storytelling in marketing. Unlike conventionally produced goods, such products have both a clear past and present product identity. Although they may vastly diverge in their form and purpose, both identities are embodied in the product. We argue that the past identity of a repurposed product amounts to the starting point of its biographical story of transformation, and that this holds storytelling potential. We refer to this strategy of alerting customers to a product's past identity as "past identity salience" and argue that it increases demand because it triggers narrative product thoughts and allows customers to feel special with the storied product. Notably, the past identity involves a waste product (e.g., a broken mosquito net) that may not serve the product's current primary function (e.g., a wallet made from a broken mosquito net). Even though a product's past identity is effectively useless (and potentially even disgusting), we suggest that marketers highlight it because it unlocks the product's storytelling potential.

Results of three field experiments, four experiments conducted in controlled settings, and several replication studies support these claims. Our evidence pertains to different proxies of demand, stretches across multiple product categories and past identities, holds at both the store and product level, and emerges across different methods of making the past identity salient. We rule out several alternative accounts (perceived environmental friendliness, interestingness, surprise, originality, authenticity, a handmade effect, and informational value) and deduce two relevant boundary conditions. When a mere glance at the product allows customers to discern its past, simple appeals to the past identity do not further increase demand. Moreover, making the past identity salient only increases demand if the product had a prior life—and thus, a starting point of its biographical story. This applies to upcycling and the closely related practice of recycling ([55]; [86]) but not to conventionally produced products.

The present research offers important contributions to the storytelling literature in marketing. First, we show that marketers can utilize products' own biographical stories rather than craft stories around product use or brand values, which dominate existing practices ([ 3]; [27]; [71]; [79]). Second, and in line with the story-prone nature of the human mind ([10]; [15]), we show that storytelling does not require explicit detail. Simple cues suffice for customers to note the presence of a story and essentially tell it to themselves ([40]). Notably, we show that this boosts demand even if the starting point of these stories is prosaic, such as a past identity as a mundane pallet or an old mosquito net. Third, and drawing on the finding that storied objects are deemed special ([34]), we identify a rarely examined key mechanism that underlies our effects on demand: storied products provide customers with felt specialness.

We also contribute to the growing literature on products that entail the use of materials with a prior life and identity ([ 1]). As one of the first empirical studies of upcycling from a marketer's perspective, we highlight that success is more likely if marketers focus on the special biographies of upcycled products as their unique selling proposition ([46]). This is an important contribution, because it also extends to recycling and provides marketers of upcycled and recycled goods with actionable techniques to increase customer demand—an effort that is needed to realize the environmental and economic potential of these market practices ([48]).

Upcycling is a sustainable production mode that prolongs the life of old objects by creatively reusing and reshaping them into new products ([13]; [85]). This repurposing practice allows brands to tap into new markets and generate value from what otherwise might be waste ([12]; [30]; [88]).

Upcycling shares this benefit with other sustainable practices of product reuse, such as secondhand products ([ 2]; [45]; [46]), vintage products ([20]; [82]), or social recycling ([22]). However, these practices amount to the simple reuse of the same product by a different owner. In contrast, upcycling entails repurposing old products and results in a new product. It is thus similar to recycling, wherein the value is in old materials being transformed by breaking them down into raw materials before turning them into new products again ([12]; [77]; [84]). Both upcycling and recycling involve the repurposing of old products and entail a true and substantive transformation, in which the nature of the outcome product differs from the nature of its discarded source product.

As a result of this transformation, repurposed products have two differing identities: a past identity, which is derived from the form and functionality of the source product, and a present identity, which captures the product's current and mostly different form and functionality. While marketers can focus on the upcycled product's benefits and emphasize elements of its present identity, they can also highlight the product's now dysfunctional past and draw customers' attention to the old or waste materials that compose the product. We call this strategy of alerting customers to the repurposed product's past identity "past identity salience." For example, an ad for a wallet made out of old mosquito nets could either not mention this past or explicitly state that it is made from mosquito nets, which is what some upcycling brands do. For example, the luxury bag brand Elvis & Kresse (www.elvisandkresse.com) prominently references to the past identities of its products in its communications, and the Swiss brand Freitag leverages the fact that its bags and accessories are made from truck tarps (www.freitag.ch). The Berlin-based store Upcycling Deluxe even enables its customers to search for products on the basis of what they used to be (www.upcycling-deluxe.com). Recycling brands, in contrast, frequently draw attention to the recycled nature of their products but do not disclose their specific past identities. It is still unclear whether the demand for repurposed products benefits or suffers from a strategy of "past identity salience."

Several findings actually discourage highlighting repurposed products' past identities. Many customers are skeptical about purchasing used goods ([41]), and they can be sensitive to the physical distortion of a product, which makes them dispose of it more quickly ([77]). Indeed, upcycled products often show traces of wear and tear from their original purpose, which makes customers aware that they are not the first person to interact with the product. Drawing attention to the product's past identity may thus elicit processes of contagion that prevent customers from opting for a product contaminated by other people ([ 5]; [56])—a danger that has been observed in the context of product reuse ([45]) and could extend to repurposing.

However, we suggest that it does not, and that repurposed products escape the stigma of the past because they have effectively been transformed into a new product (see [86]), who find that thoughts of transformation trigger recycling). Through this transformation, a salient past identity not only fails to harm demand but, on the contrary, fuels demand, and we propose that this is because past identity salience draws attention to the product's special story. Repurposed products can serve as a prime example of how past identity salience can trigger the persuasive story of a product's biographical transformation.

This argument is new to the storytelling literature in marketing. To support it, we first need to address the question "What is a story?" Essentially, a story is a linear temporal sequence of causally related events ([27], [28]). It is a chronological description of which events occurred and how they are connected. Another term consumer researchers have used to describe this is "narrative" ([ 3]; [27], [28]). Note, however, that other literature has distinguished between these terms in that "story" refers to a chronological chain of events and "narrative" to their causal factors ([68]; [73]).

Chronology and causality are the central structural characteristics that enable narrative thinking ([19]; [27], [28]; [73]). Chronology refers to the temporal sequence or episodes of events ([65]) and to the fact that stories have a beginning, a middle, and an end ([28]; [74]). Causality is defined as the causal connections or relationships between story elements ([27]) that enable customers to assign meaning to a narrative. Rather than causality, some authors use the term "plot," which refers to the theme of a story and imbues story events with meaning ([65]). Stories can feature any number of specific story elements, which are woven together in a plot. Several typologies have been offered to explain the plots of stories throughout history (e.g., [ 9]; [16]; [76]). Although plots differ, a prototypical narrative features a protagonist who is the main character of the story ([65]), such as Cinderella, who participates in story events ([19]). Notably, the mere presence of a main character can serve as a means to provide causality ([19]; [79]).

Humans have been attuned to stories and narrative thinking since the dawn of humankind and stories wield considerable power over people ([15]). They are able to demonstrate, communicate, and persuade ([27], [26]). The marketing literature has been well aware of stories' ability to fuel demand ([79]; [83]; [87]), and storytelling is a common marketing practice ([71]; [80]). Marketers tell brand stories ([71]) and stories in which products affect consumers ([49]; [83]), and artists tell stories that feature products or brands as contextual elements or accessories, as in the case of product placement ([44]; [70]). In most of these stories, the product is not the main protagonist, and all of them are clearly recognizable as fully fledged stories that someone tells.

Advancing current theorizing, we suggest adding the product's own biographical story to marketers' storytelling toolbox. Moreover, we suggest that marketers can trigger these stories without spelling them out. Simply making the product's past identity salient, as we propose, can induce customers to infer a repurposed product's biographical story and in turn increase demand for the storied product.

But how can customers comprehend such stories when an ad features no more than the product and a reference to its past identity? The answer lies in the fact that humans are uniquely attuned to engage in narrative thinking and discern, self-tell, and appreciate a good story ([ 3]; [27]). Moreover, even simple past identity appeals map onto the key structural story characteristics of chronology and causality. As to chronology, a product's past identity is an episode in its life that chronologically precedes its present identity. Making the past identity salient thus ensures that multiple chronologically ordered episodes in the product's life become salient. As to causality, salience of the product's past and present identities implies their causal connection. The repurposed product is the protagonist that ensures causality through its implied identity transformation.

Transformational stories are historically one of the most popular forms of story ([16]; [67]). They involve a change in identity as part of the protagonist's biography. Also known as metamorphosis ([76]), this type of plot involves a temporally bounded event (i.e., the transformation) wherein the protagonist changes from one permanent state to a new permanent state without the disappearance of the protagonist from the story ([40]). Metamorphosis plots are present throughout history ([33]), ranging from stories from antiquity (e.g., Ovid's Metamorphoses), to more recent novels (e.g., Franz Kafka's The Metamorphosis), fairy tales (e.g., Cinderella), and popular culture (e.g., The Incredible Hulk, The Matrix). Notably, it also extends to the biographical stories of repurposed goods, which transform from being a depreciated product on the verge of the waste-bin to a storied new object with multiple identities.

Transformational stories are particularly powerful in terms of inducing narrative thoughts. They only need what narratologists term a "minimal narrative" to unfold in people's minds. A minimal narrative consists of identical entities that are present in two temporally and qualitatively distinct states ([66]). The essence of minimal narratives is time change and, often, transformation ([54]). In our research, the minimal narrative consists of the protagonist in time 1 (the salience of the product's past identity) and the transformed protagonist in time 2 (the upcycled or recycled present product identity). Minimal narratives are more than a mere ordered sequence of events because of their overall meaning ([54]), which unfolds in perceivers' minds.

We propose that customers will infer a repurposed product's story when its past is salient because this salience allows for an awareness of the plot (i.e., how the different identities are connected via the transformation). To attain that meaning, customers need to engage in inferential processing. Inferencing forms the narrative linkages that empower minimal narratives (past identity, present identity). To engage in inferential processing, individuals need to draw on their personal knowledge and imagination. In a dynamic process, individuals interact with the presented story elements, infer missing information, and include past information to disambiguate stories ([32]; [42]). Research in psychology and consumer research shows that humans are genuinely prone to take agency and engage in inferencing and using their imagination to generate story causality ([27]; [35]; [79]) and to thus complete or comprehend a story that is not fully spelled out.

A plot of transformation provides a particularly rich resource for inferential narrative processing. The multiple identities and the metamorphosis itself allow for multifaceted interpretations ([32]; [42]) and provide perceivers with a wide projection; inferencing; and, thus, storytelling space. We therefore expect that a salient cue for a repurposed product's past identity will suffice to induce perceivers to infer its biographical story of transformation.

Why would people demand a product that holds a story of having been waste more than a product that does not? We propose that storied products cause higher demand because they imbue customers with felt specialness—that is, the belief that they will feel more special as a result of acquiring and utilizing a product that holds a story.

To understand how stories can evoke a sense of specialness in people, it is important to understand the ways in which stories affect people ([15]; [27]; [49]). On the one hand, stories focus them on the narrative rather than on rational arguments ([49]), potentially even transporting (i.e., absorbing) recipients into a story ([36]; [79]). However, for this to happen, people must experience the pathos of a dramatic story ([64]) and feel empathy for story characters ([79]). This tends to necessitate the act of telling a fully-fledged story to passive recipients. The minimal narrative provided by quick exposure to an ad including past identity cues is unlikely to allow for either of these experiences.

On the other hand, and largely fueled by inferential processes, stories help people in their sensemaking process ([87]). This mechanism is well-suited to explain the appeal triggered by minimal narratives. People's narrative thoughts help shape their perceptions of the story's protagonist and its meanings ([27]). When stories are self-inferred, they are personal to the individual ([74]; [83]) and can evoke diverse special and individual meanings ([57]; [69]). For example, if a customer sees an ad for a wallet made from an old mosquito net, they might draw on their own associations and think of the lives the net has saved or connect it with their own personal travels. These self-inferred, special meanings help decommodify the object ([25]; [46]).

Simply "having" a story suffices for the protagonist to become more special. Cinderella, for example, is a special princess thanks to her story of transformation (see [31] for how biographical transformations affect the appreciation of the "transformer"). Like Cinderella, repurposed products are the protagonist in a transformational story that imbues them with a unique biographical history, and objects with such a story are likely to be perceived as special ([46]), even by young children ([62]).

Importantly, the meanings of objects tend to transfer to those who acquire and utilize them ([52]) and can help individuals in their identity work, a process that often motivates the decision of whether to acquire an object ([ 4]; [ 7]; [14]). Storied—and thus, special—objects have the power to make individuals feel special about themselves. Given that objects that promise feelings of specialness are known for being high in demand ([ 8]; [38]), storied objects are likely to boost demand ([34]; [58]).

In summary, we propose that making a repurposed product's past identity salient alludes to the minimal narrative of its transformational story. This invites customers to engage in narrative thinking and allows them to infer an individualized and special version of the product's story. Perceiving the product as storied, in turn, enables customers to feel special with the product, which eventually triggers demand. Figure 1 provides an overview of the propositions made.

Graph: Figure 1. Conceptual model.

We test our propositions in seven studies. Studies 1a, 1b, and 2 provide evidence for the effect of past identity salience on real-world product demand. Studies 1a and 1b investigate this in actual Facebook campaigns. Study 2 examines sales data for an experimental upcycling pop-up store. Studies 3, 4, and 6 demonstrate that past identity salience increases demand because the product's biographical story affords people with specialness and generalizes our findings to different types of products and past identities as well as the various claims that make these identities salient.

We also provide evidence of the limits of past identity salience. Marketer-crafted allusions to a product's past identity are not the only means through which individuals may infer a product's biographical story. Products with an easily discernible past identity, such as a bag sewn out of highly visible candy wrappers, already provide all the cues needed for the story to unfold in a customer's mind. We thus do not expect additional appeals to a product's past identity to increase demand when the past identity is already salient. Study 5 demonstrates that visual discernibility of a product's past identity acts as a moderator.

Given that any biographical transformation requires a past identity, we also do not expect results to generalize to genuinely new products. Study 6 thus compares different production modes and shows that our effect is specific to repurposed products. In particular, it extends our findings to the more prevalent market practice of recycling and demonstrates that they do not generalize to conventional modes of production.

Finally, we address several alternative explanations across studies. Study 3 rules out the possibility that the effects are driven by increased perceptions of environmental friendliness. Study 4 shows that results are not a manifestation of a handmade effect, and Study 6 addresses the possibility of other plausible confounds (perceived originality, authenticity, surprise; see also the Study 2 posttest in the Web Appendix). All stimuli (Web Appendix W1), additional analyses (Web Appendix W2), supplementary studies (Web Appendix W3), and replication studies (Web Appendix W4) are included in the Web Appendix.

In Study 1, we use two online field experiments to examine how people respond to Facebook ads that make the past identities of upcycled products salient. In Study 1a, we examine how these ads affect Facebook page likes. In Study 1b, we look at the effects on clicks.

To ensure ecological validity, we teamed up with an upcycling store and jointly created two Facebook ad campaigns. The objective of the first campaign (Study 1a) was to increase the number of likes of the store's Facebook page. The objective of the second campaign (Study 1b) was to drive traffic to an external website featuring an online voucher promotion. Each campaign targeted people between the ages of 18 and 65 years old (Facebook estimated a potential target audience of 1.34 million individuals) living in the store's vicinity. To prevent people from being exposed to both campaigns on the same day, we activated only one at a time for seven days and six days, respectively.

We created small rectangular ads that featured different upcycled products from the store: a cake stand made from old pot lids, a vase made from a light bulb, and a pen holder made from used forks (Web Appendix W1). We manipulated past identity salience by stating what the products used to be ("I used to be a...pot lid, light bulb, fork"). The control group read: "Now I am...a cake stand, a vase, a pen holder." By using the term "Now I am," it also hinted at a transformation but made the products' present identities salient.

In Study 1a, we measured unique and total like rate (the number of page likes the ad generated relative to its unique and total reach). In Study 1b, we measured unique and total click rate (the number of clicks the ad generated relative to its unique and total reach). To make measures comparable and to control for variance in unique reach (total number of unique people who saw the ad) and total reach (total number of times the ad was shown), we used relative measures (i.e., likes/clicks in percentages of reach) in both studies.

Table 1 presents a summary of campaign statistics and shows ad performance across conditions. Because we only had access to aggregate behavioral data, we conducted two-sample proportions z-tests to determine which ad was relatively more successful.

Graph

Table 1. Descriptives of Appeal Measures by Condition (Studies 1a, 1b, and 2).

As expected, the ad yielded a higher unique (.16% vs..06%; Z = 6.72, p <.001) as well as total like rate (.06% vs..03%; Z = 4.65, p <.001) when the products' past identities were made salient.

A similar pattern emerged for the promotion campaign. Past identity salience significantly increased unique (.29% vs..26%; Z = 1.91, p =.06) and total (.11% vs..08%; Z = 4.97, p <.001) clicks on the promotion.

Drawing on ecologically valid experimental field evidence in an online context and featuring a portfolio of different upcycled products, Studies 1a and 1b show that past identity salience can increase demand for products of an upcycling store. Owing to the context in which low levels of engagement are common (for average clickthrough rates, see [17]]), absolute effect sizes were quite small, even though like rates doubled and click rates increased by more than 37%. We designed Study 2 to provide ecologically valid field evidence in a context that allows for more pronounced absolute differences.

Studies 1a and 1b measured online interest as a demand proxy. Study 2 extends the inquiry to an experimentally controlled brick-and-mortar context and actual sales data.

In collaboration with two upcycling stores, we set up our own upcycling pop-up store on the campus of a large European university. The assortment included 24 different upcycled products (e.g., bags, wallets, bowls). Items were made from a variety of source products (e.g., mosquito nets, parachutes, bicycle tubes) and priced between €5 and €85. Study participants were all potential customers who passed the shop. The shop was open over six days for five hours each day during the Christmas season in 2017. We hired two sales assistants to run the shop on alternate days. To allow for a constant setup, we provided them with a sales script. We instructed them to be friendly but passive (no sales pitches) and to discuss the product's past identity only when prompted by customers. We also instructed assistants to take notes of all that happened during the day.

We manipulated past identity salience by alternating the marketing materials at the point of sale. These included ( 1) a leaderboard that highlighted the products' past ("We used to be...parachutes, truck tarps, pot lids, etc."; experimental condition) or present ("We are...bags, wallets, cake stands, etc."; control condition) identity, ( 2) a corresponding price list with individual price tags and product descriptions, ( 3) product flyers, and ( 4) a promotional poster (Web Appendix W1). We changed conditions on a daily basis.

For each day, we used the receipt data to assess the number of purchases, the number of products sold, and total revenue. To account for any effects associated with general customer frequency and engagement, the sales assistants tracked the number of visitors to the shop with a manual clicker, self-assessed the busyness level at the shop site every 30 minutes (1 = "not very busy," and 7 = "very busy"), and measured conversation time per visitor.

Average busyness at shop site (M_salient = 2.55, SD = 1.17; M_control = 2.77, SD = 1.34; t(58) =.46, p =.65) and conversation time per visitor (M_salient =.56 min, M_control =.50 min; z = 1.09, p =.28) did not differ across conditions. When the past identities were made salient at the point of sale, however, the shop had approximately 60% more visitors (266 vs. 165; Mann–Whitney U-test: z = 1.96, p <.05); triple the amount of purchases (25 vs. 8; z = 1.99, p <.05); four times more products sold (36 vs. 8; z = 1.99, p <.05); and, as a result, more than quadruple the revenue (€572 vs. €127; z = 1.96, p <.05). Moreover, the conversion rate (proportion of visitors making a purchase) was nearly twice as high when the products' past identities were made salient (9.4% vs. 4.8%; z = 1.63, p =.08, Table 1).

Making the past identities of the products salient to customers in an actual upcycling store increased demand beyond our expectations and with regard to every single factor that can increase revenue: interest (i.e., visitors), conversion, and sales volume. To explore the underlying dynamics of these effects, we followed up on Study 2 with a posttest (Web Appendix W3), which revealed that our effects are unlikely to have been driven by how interesting, boring, or surprising people perceived the products to be.[ 6] Instead, and in line with our assumptions, people feel more special with the products, find them more appealing, and are more likely to purchase them when products' past identities are made salient.

Having established real-world support for our proposed main effect, Study 3 aims to show that past identity salience affects demand because it increases customers' felt specialness with the product. We proposed that past identity salience would increase felt specialness because it triggers thoughts about the product's biographical story. To test for this, we also qualitatively explore whether past identity salience triggers narrative thoughts—and, if so, what elements of the product's story these thoughts refer to. Finally, we address the alternative explanation of felt environmental friendliness. Repurposing products is a proenvironmental practice. Stressing the product's past identity may thus play into sustainable purchase motives ([60]). We aim to rule out that past identity salience may drive demand because it increases felt environmental friendliness rather than specialness.

Two hundred twenty-four U.S. panelists from Amazon Mechanical Turk (MTurk; 44% female, M_age = 35 years) were instructed to evaluate a backpack upcycled from an old airbag in a one-factor (past identity: salient vs. control) between-subjects experiment. We manipulated past identity salience as in prior studies (see Web Appendix W1) and informed all participants that the backpack was upcycled. Participants in both conditions thus rationally knew that the product had a past identity, but this was made salient and concrete in only one condition.

As proxies of demand, we first assessed the backpack's appeal ("How would you evaluate this product?" 1 = "unappealing/don't like it at all," and 7 = "appealing/like it a lot"; α =.95) and participants' purchase intention ("Would you buy this product?" 1 = "No, definitely not," and 7 = "Yes, definitely"). We measured felt specialness as the focal process variable (three items adapted from [53]]): "How special/unique/recognized would you feel with this product?" 1 = "not at all," and 7 = "very"; α =.91) and felt environmentalism as an alternative process (three similar items: "How sustainable/environmentally conscious/environmentally friendly would you feel with this product?" 1 = "Not at all," and 7 = "Very"; α =.93). To explore whether an appeal to the past identity would trigger narrative thoughts, we asked participants to describe what they thought was special about the product in an open-ended question. Finally, we asked whether they took the study seriously and answered conscientiously. Two participants who had indicated that they did not were excluded from further analyses.

In line with previous results, past identity salience increased product demand. In particular, participants perceived the backpack as more appealing (M_salient = 4.80, SD = 1.84; M_control = 4.04, SD = 1.94; t(220) = 3.01, p <.01) and were more likely to purchase it (M_salient = 4.17, SD = 2.00; M_control = 3.43, SD = 2.07; t(220) = 2.70, p <.01) when its past identity as an old airbag was made salient.

Participants also perceived the backpack as more specialness-affording (M_salient = 4.34, SD = 1.68; M_control = 3.82, SD = 1.81; t(220) = 2.22, p <.05) and felt more environmentally friendly with the backpack (M_salient = 5.37, SD = 1.33; M_control = 4.29, SD = 1.70; t(220) = 5.28, p <.001) when its past identity was made salient.

To test for the proposed effect of past identity salience on demand through specialness, we conducted two bootstrap mediation analyses (Model 4, [39]). We entered past identity salience as the independent variable (0 = control, 1 = salient), felt specialness as the mediator, and the respective demand variables as dependent measures. Because past identity salience also affected how environmentally friendly people felt, we included it as an alternative mediator. We found an indirect effect of past identity salience on product appeal (indirect effect =.40; 95% confidence interval [CI₉₅] = [.05,.79]) and purchase intention (indirect effect =.44; CI₉₅ = [.06,.86]) through felt specialness. Felt environmentalism did not mediate the effect on appeal (indirect effect = −.02; CI₉₅ = [−.23,.20]) or purchase intention (indirect effect =.002; CI₉₅ = [−.19,.20]). It can thus be ruled out as an alternative process.

Finally, we analyzed the open-ended answers to explore differences in narrative thinking across conditions. All answers were coded in terms of whether or not they signaled narrative thinking and in terms of their content elements. Thoughts that hinted at chronology and causality, the central structural characteristic of narrative thinking, were coded as narrative, whereas other thoughts were coded as descriptive. As to the content elements, eight recurring themes emerged. Three narrative thought topics focused on different story elements. The respective codes are past identity, in which the prior life of the product was prominent; metamorphosis, in which the product's transformation was prominent; and other biographical elements, in which chronology and causality of the product's story were present but neither could be clearly identified as central. The five descriptive thought topics comprise type of material, production mode, environmental aspects, other product attributes, and product evaluations (for descriptions and examples, see Table 2). Two independent raters who were blind to the condition coded responses with regard to the presence of each of these codes. More than one code could be assigned to one response, and remaining disagreements were resolved through discussion (interrater reliability: all κs >.61).

Graph

Table 2. Study 3: Thoughts and Themes Associated with Product Specialness by Condition.

1 ^a Numbers in brackets refer to participant numbers.

Table 2 shows the relative prevalence of codes across conditions. Despite the fact that all participants knew that the backpack had a history, narrative thoughts were more pronounced when the ad made the product's past salient. Participants in the past identity salience condition more often reported narrative thoughts (69.6%) than those in the control condition (2.8%; χ² = 105.53, p <.001). In particular, they more often referred to the product's past identity (66.1% vs. 1.9%; χ² = 100.30, p <.001), its metamorphosis (50.4% vs..9%; χ² = 69.60, p <.001), and other biographical story elements (17.4% vs..9%; χ² = 17.53, p <.001). In addition, descriptive thoughts about the production mode became more prevalent (34.8% vs. 15.9%; χ² = 10.37, p <.01) when the past life of the product was made salient. In contrast, descriptive thoughts of general product attributes (37.4% vs. 61.7%; χ² = 13.09, p <.001) and evaluations (21.7% vs. 35.5%; χ² = 5.18, p <.05) were more prevalent in the control condition. Thoughts about the product's material (19.1% vs. 18.7%; χ² =.01, p =.93) or its environmental aspects (7.8% vs. 4.7%; χ² =.93, p =.33) were equally prevalent across conditions.

Study 3 replicates the effect of past identity salience on product demand in a controlled setting and supports our proposed account. Making the past identity of an upcycled product salient boosted its specialness-affording potential, which, in turn, increased product appeal and purchase intention. Although past identity salience also affected how environmentally friendly people felt with the product, environmental friendliness did not mediate the effect.[ 7] In addition, Study 3 provides qualitative insights into why customers feel more special once the past identity of a repurposed product is made salient. Past identity salience appears to trigger the product's story and brings to mind additional narrative thoughts that relate in particular to the product's past identity and metamorphosis while decreasing evaluative descriptions and focus on other attributes such as design or weight.

Study 4 provides additional evidence about the proposed process. It tests whether past identity salience enhances people's perceptions of the product as storied, which in turn induces felt specialness and demand (see Figure 1). To corroborate the role of felt specialness as a driver of demand, we not only measured felt specialness but also tried to moderate it. If felt specialness drives the effect, then this route should be less pronounced for those who feel very special already. In addition, Study 4 ensures robustness of the results by extending the inquiry to minimal appeals (i.e., simple "made from" claims that neither spell out the story directly nor depict the past identity) and to a new product category and different source product (i.e., a wooden table made from a pallet). Finally, it also addresses another viable alternative explanation: it is possible that highlighting the past identity of a product induces people to think that the product is handmade, a product characteristic that is known to increase product attractiveness ([29]).

A total of 98 MTurk workers (41% female; M_age = 37 years) were randomly assigned to one of two ads that promoted a wooden table (Web Appendix W1). In the past identity salience condition, the ad read, "I was made from an old pallet"; in the control condition it read, "I was made for dining." Prior to ad exposure, we assessed baseline feelings of personal specialness with the same three items used to capture product-specific felt specialness ("In general, how special/unique/recognized by others do you feel?" 1 = "not at all," and 7 = "very"; α =.86). After ad exposure, we measured product appeal (α =.93) and purchase intention as in Study 3. To assess whether past identity salience triggers narrative thoughts about the product and its biography, we adapted four items from [49] ("The product tells a story," "The product's story has a beginning, a middle and an end," "The product has evolved over time," "The story of the product has a chronological order"; 1 = "Strongly disagree," and 7 = "Strongly agree"; α =.95). Finally, we measured felt specialness, as in Study 3 (α =.93), and assessed the degree to which participants thought that the product was homemade ("This product looks..." 1 = "homemade," and 7 = "made by a company").

When the past identity of the wooden table was made salient, participants perceived it as more appealing (M_salient = 5.15, SD = 1.56; M_control = 4.29, SD = 1.69; t(96) = 1.80, p <.05) and were more likely to buy it (M_salient = 5.21, SD = 1.50; M_control = 4.48, SD = 1.70; t(96) = 2.25, p <.05). Past identity salience also increased product story perceptions (M_salient = 5.13, SD = 1.30; M_control = 3.89, SD = 1.95; t(96) = 3.71, p <.001) and made participants feel more special with the product (M_salient = 4.85, SD = 1.49; M_control = 3.90, SD = 2.00; t(96) = 2.66, p <.01). Felt personal specialness prior to ad exposure (M_salient = 5.04, SD = 1.48; M_control = 5.02, SD = 1.37; t(96) =.07, p =.95) and perceptions of the product as handmade (M_salient = 4.32, SD = 2.03; M_control = 4.67, SD = 1.94; t(96) = −.87, p =.39) did not differ across conditions. Note that the latter also did not moderate the effect of past identity salience on demand (see additional analyses in Web Appendix W2).

To corroborate the role of felt specialness, we ran moderated mediation analyses (Model 7, [39]) with past identity salience as the predictor (0 = control, 1 = salient), felt specialness as the mediator, product appeal and purchase intention as the outcome variables, and baseline personal specialness as a continuous moderator (M = 5.03, SD = 1.42). In support of our propositions, felt specialness mediated the effect for people low (−1 SD) and average (mean) in felt personal specialness, but not for people who already felt very special to begin with (+1 SD) (see Table 1 and Web Appendix W2).

We next tested whether product story perceptions led to an increase in felt specialness and, as a result, in demand. Two bootstrap sequential mediation analyses (Model 6, [39]) with past identity salience as the independent predictor (0 = control, 1 = salient), product story and felt specialness as sequential mediators, and product appeal and purchase intention as outcome variables found evidence for sequential mediation via product story and felt specialness on product appeal (indirect effect =.15, CI₉₅ = [.06,.27]) and on purchase intention (indirect effect =.15; CI₉₅ = [.05,.28]). Notably, both individual indirect effects through product story and felt specialness on demand became nonsignificant (CIs include 0). Moreover, switching the order of the mediators resulted in a nonsignificant mediation (CIs include 0). This supports the proposed sequential mediation chain.

Results of Study 4 rule out the competing explanation that our effects are due to the handmade effect ([29]) and fully support our proposed process. Even a simple "made from [past identity]" claim increased demand, and this was due to the claim imbuing the product with a story, which in turn made customers feel more special with the product. Study 4 thus supports our proposition that a product's past identity holds storytelling potential. It also corroborates the central role of felt specialness as our underlying process. Baseline felt specialness moderated the effect of past identity salience on specialness and, as a result, the indirect effect of past identity salience on demand.

Our findings suggest that past identity salience is effective because it imbues products with a prior life with their specialness-affording biographical story. However, what if the product itself already tells the story (i.e., the past identity is visually discernible and salient in the product)? Our theorizing suggests this to be a relevant boundary condition for the power of ad-induced past-identity salience. In Study 5, we test for this boundary condition. Specifically, we used a vase made from an obviously discernible light bulb and a vase made from a less discernible electric insulator (for a replication across product categories, see Web Appendix W4). We expect our effect to generalize to the insulator vase but to be attenuated for the light bulb vase because its past life is already salient.

We recruited 562 volunteers from a university mailing list who were familiar with upcycling (61% female; M_age = 24 years) to participate in a 2 (past identity: salient vs. control) × 2 (past identity discernibility: subtle vs. discernible) between-subjects experiment. Depending on condition, participants saw an ad that either did or did not highlight the past identity of a vase made either from an easily discernible light bulb or from a less discernible electrical insulator (see Web Appendix W1).

We measured product appeal ("How much do you like this vase?" 1 = "don't like it at all," and 7 = "like it a lot"), purchase intention ("If you were looking for a vase, would you buy this particular one?" 1 = "No, definitely not," and 7 = "Yes, definitely"), and willingness to pay (WTP; "What is the maximum amount you would pay for this vase?" open-ended). As a study incentive and a measure of behavioral product demand, participants could choose to win a product of their choice at the end of the study: the promoted upcycled or a conventional vase.

A two-way analysis of variance on product appeal produced a main effect of past identity salience (F( 1, 558) = 8.19, p <.01) and a main effect of past identity discernibility (F( 1, 558) = 34.60, p <.001) on product appeal. The prior effect is in line with previous results, and the latter is indicative of product differences. Importantly, these effects were qualified by a marginally significant interaction (F( 1, 558) = 3.41, p =.06). Planned contrast comparisons revealed that past identity salience increased appeal when the past identity of the vase was not discernible (M_salient = 4.03, SD = 1.68; M_control = 3.37, SD = 1.60; t(280) = 3.36, p <.01). It did not, however, affect the appeal of the visibly discernible vase (M_salient = 4.59, SD = 1.68; M_control = 4.45, SD = 1.65; t(278) =.71, p =.48).

A two-way analysis of variance again produced a main effect of past identity salience (F( 1, 558) = 10.99, p <.01) and past identity discernibility (F( 1, 558) = 20.56, p <.001), qualified by a marginally significant interaction effect (F( 1, 558) = 3.63, p =.06). Making the past identity salient boosted demand when it was difficult to discern (M_salient = 3.18, SD = 1.74; M_control = 2.46, SD = 1.46; t(280) = 3.78, p <.001) but not when it was visibly discernible (M_salient = 3.55, SD = 1.67; M_control = 3.35, SD = 1.69; t(278) =.98, p =.33).

To assess effects on the highly skewed WTP measure, we built three equally sized groups of amounts that participants were willing to pay for the vase (<€5, €5–€10, ≥€10; Figure 2, Panel A). Chi-square tests per product show that past identity salience led to a significant increase in the number of people who were willing to pay more than €10 (57% vs. 43%; χ² = 5.53, p =.06) when the past identity was difficult to discern, but this did not affect WTP when the past identity was visible to begin with.

Graph: Figure 2. Effects of past identity salience (Study 5).

Finally, we ran a logistic regression with past identity salience, past identity discernibility, and their interaction term as the predictors, and product choice as the outcome. We found a significant interaction effect (Wald = 3.70, B =.68, p =.05) but found neither a main effect of past identity salience (Wald = 2.24, B = −.82, p =.13) nor a main effect of past identity discernibility (Wald = 1.29, B = −.62, p =.26). Past identity salience increased the choice of the upcycled product when its past identity was hard to discern (χ² = 4.42, p <.05) but not when it was easily discernible (χ² =.36, p =.55; Figure 2, Panel B).

Study 5 demonstrates that making the past identity salient is effective, particularly when customers cannot easily discern this identity and infer the product's biographical story by simply looking at it.[ 8] When the past identity was discernible, emphasizing it did not boost demand further. To see how this boundary condition can nonetheless be overcome, see Studies 4a and 4b in Web Appendix W3, which also rule out that a salient past identity could be effective simply because it offers more information.

Study 5 showed that appeals to a product's prior life primarily work if this prior life is not salient already. Study 6 addresses another managerially relevant boundary condition. So far, we have tested our propositions in the context of upcycled products, which doubtlessly hold prior identities. In Study 6, we thus ask whether the effect is indeed specific to repurposed products (i.e., products with a prior life). To do so, we generalize our inquiry to recycled products, which are also made from products with a prior life, and conventional products, which are made from brand-new raw materials that lack such a prior life. We expect results to generalize to recycled products but not to conventional products. In addition, we control for various alternative accounts. It is plausible that making the past identity of a product salient might simply trigger surprise and perceptions of product originality or authenticity.[ 9] All of these are connected to positive customer responses and have been studied in the context of vintage products, which are—like upcycled and recycled products—characterized by strong past identities (e.g., [20]; [78]; [82]). Moreover, surprise and novelty have been connected to storytelling ([27]).

We used the same backpack as in Study 3 and randomly assigned 163 individuals (57% female; M_age = 32 years) to one of four product ads, which manipulated production mode and thus the backpack's past identity (Web Appendix W1). All conditions featured the same slogan "I am a trendy backpack." In the upcycling condition, participants also read, "In my previous life, I used to be an airbag." In the recycling condition, they read, "In my previous life, I used to be old plastic," and in the conventional condition, they read, "In my previous life, I used to be polyester." Past identity was not made salient in the control condition; it only read, "I am a trendy backpack."

Following ad exposure, we first assessed demand variables. Product appeal (α =.94) and purchase intention were measured as in Study 4, and WTP was measured on a slider scale ranging from €0–€200. In addition, we assessed relative purchase intention by asking participants to rank different backpacks according to their purchase likelihood (1 = "highest" to 4 = "lowest" likelihood). These backpacks included our focal backpack from the ad and three additional backpacks participants had not been exposed to before. To corroborate the process, we also measured perceptions of product story (α =.90) and felt specialness (α =.93) as in Study 4. As a manipulation check, we added three items assessing the degree to which participants perceived that the product had a prior life (i.e., a true past identity; "The product contains..." 1 = "little history/past/identity," and 7 = "a lot of history/ past/identity"; α =.94). Finally, we assessed perceived originality ("This product is novel/original/different"; α =.78), adapted from [47], as well as surprise ("The product surprises me/is unexpected"; α =.91) and authenticity ("The product is authentic/genuine and real"; α =.89) adapted from [43] and [59], all on seven-point scales (1 = "Strongly disagree," and 7 = "Strongly agree").

Table 3 provides means, standard deviations, and pairwise comparisons of all assessed variables by condition. Conditions differ with regard to all assessed variables.

Graph

Table 3. Study 6: Means Across Conditions (Past Identity Salience of Different Production Modes).

2 Notes: Different superscripts indicate different cell means (p <.05) based on planned contrast comparisons.

In confirmation of the intended manipulation, we found stronger past identity perceptions in both the upcycling and recycling conditions than in the conventional backpack and control conditions (F( 3, 159) = 33.12, p <.001).

Conditions also significantly differ on product appeal (F( 3, 159) = 7.25, p <.001), purchase intention (F( 3, 159) = 7.41, p <.001), WTP (F( 3, 159) = 3.72, p <.05), relative purchase intention (F( 3, 159) = 4.32, p <.01), product story perceptions (F( 3, 159) = 12.48, p <.001), felt specialness (F( 3, 159) = 24.60, p <.001), product originality (F( 3, 159) = 4.32, p <.01), surprise (F( 3, 159) = 4.32, p <.01), and authenticity (F( 3, 159) = 4.32, p <.01). Compared with the control group, all measures of demand were higher in both the upcycling and recycling conditions but not for the conventional backpack. Likewise, product story perceptions and felt specialness were significantly higher when the product was upcycled or recycled but not when it was conventionally produced. The same significant pattern of results emerged for our alternative processes, perceived originality, surprise, and authenticity. We thus controlled for them in all subsequent analyses.

To test whether past identity salience affects demand through product story and felt specialness, we ran sequential mediation analyses per demand variable. Production mode served as the multicategorical predictor (Model 6, [39]). We compared all groups in which we had made a past identity salient (upcycling, recycling, conventional) to the control group in which we had not done so. To control for potential confounds, we added our alternative process variables as covariates to the model (Web Appendix W2 provides further analyses ruling out these suggested accounts). We found evidence for a sequential mediation from past identity salience through product story and felt specialness on product appeal in both the upcycling (indirect effect =.09, SE =.05; CI₉₅ = [02,.20]) and the recycling (indirect effect =.14, SE =.07; CI₉₅ = [03,.30]) conditions. For these two production modes, we replicate the significant indirect effects on purchase intention (upcycling: indirect effect =.08, SE =.04, CI₉₅ = [02,.18]; recycling: indirect effect =.13, SE =.06; CI₉₅ = [03,.26]), WTP (upcycling: indirect effect =.74, SE =.43; CI₉₅ = [.03, 1.72]; recycling: indirect effect = 1.21, SE =.58; CI₉₅ = [.27, 2.55]), and relative purchase intention (upcycling: indirect effect = −.02, SE =.02; CI₉₅ = [−.05, −001]; recycling: indirect effect =.−.03, SE =.02; CI₉₅ = [−.08, −.003]). In the conventional product condition, however, neither indirect effect became significant (all CIs included 0).

Study 6 extends our findings to a different context and a new product category. It also addresses an important boundary condition. Our effects are specific to products that are—like upcycling—associated with a prior life (i.e., recycled products) but are attenuated for products that carry little past identity in them (i.e., conventional products). Study 6 also rules out important alternative accounts around perceptions of product originality, surprise, and authenticity. While past identity salience boosts all of these factors, it is the story-induced felt specialness that primarily drives demand.

If products could speak, then each product made from repurposed materials would have a rich story to tell: a Cinderella-like story of a change in identity and purpose, from a discarded and useless past identity to a vastly changed and useful new identity as a new product. We show that appealing to the past identity (i.e., a discarded, broken object that is at odds with the product's current functional purpose) fuels demand for upcycled and recycled products. This is because past identity salience draws attention to the product's special story of metamorphosis, which allows customers to feel special themselves. These insights advance our current understanding of customer reactions to goods made from repurposed materials and open the window to a new facet of powerful storytelling in marketing.

In this article, we provide novel contributions to the extant literature on storytelling and to the burgeoning literature on upcycling and recycling as two production modes that involve the use of repurposed materials. With regard to the storytelling literature, we show that one salient piece of information, the product's past identity (i.e., the beginning of its biographical story) suffices to trigger the perception of the product as having a story. We observe this regardless of how we worded this information, from the somewhat story-like "in my previous life I used to be..." (Study 5) to the less story-like "I was made from..." (Study 4), to the purely informative "upcycled from..." (Supplementary Study 2, Web Appendix W3).

In contrast to prevalent storytelling practices, which focus on stories about product use or storied metaphors of a brand's essence ([21]; [27]), we focused on the story entailed in a product's own biography. We thus extend this literature by highlighting how products can be protagonists in their own life stories. This raises the question of what such product life stories need to look like to persuade. Our evidence suggests that a product's metamorphosis, its transformation from a past identity, plays a crucial role. Metamorphosis or transformations are powerful narratives ([18]; [40]), and it seems that the source or consequence of the transformation is secondary to there being a transformation. Whatever past identity we used (from airbags to insulators) and whatever the transformation (from bags to tables), the results generalized. Just knowing that there has been a different past identity appears to suffice. We argue that it is this knowledge that invites the inference of all elements necessary for narrative thinking ([27]), a sequence of episodes (chronology) that logically build onto each other (causality).

Contrary to most current literature, we never actually spelled out the product's story. Instead, we had customers infer the story by using a "minimal narrative" of making two different identities of the same product salient ([66]). Our minimal intervention of past identity salience highlights that the mere presence of a story can be persuasive. This adds a novel facet to storytelling in marketing and advances prior literature that suggests that stories work particularly well, if people experience narrative transportation ([26]; [79])—that is, become absorbed in narrated stories (see [21]] and [49]], who also find that stories can persuade without transportation).

Why are these minimal stories persuasive? Because customers feel more special with a product that holds a biography (i.e., a story). We consistently found this, even when we controlled for novelty, surprise, authenticity, and originality. A good story increases appreciation for the story character ([ 3]; [61]) and conveys specialness ([52]). Because people want to feel special, the promise of specialness fuels demand ([38] ; [81]). This observation fits with prior insights on the specialness-affording value of object history ([20]; [46]; [82]), but it constitutes a novel insight in the domain of storytelling. Having a story to tell should thus be added among the factors known to make customers feel special (e.g., [ 8]; [38]).

The power of the mere presence of a product's biographical story also allows our insights to be positioned with regard to different production modes. On the one side, our results help distinguish upcycling (our key phenomenon) from consumption of secondhand goods and vintage items, in which a product's prior life matters greatly and contagion is an issue ([ 2]; [37]; [45]). In contrast, upcycling encompasses the presence of a radical transformation away from a product's past identity. This transformation eliminates any taint associated with an upcycled product's past, although this past still makes it special. Notably, our findings generalize to recycling, in which products with past identities are broken down prior to being transformed into new products ([55]). A process of transformation thus appears to be a managerially relevant conceptual distinction within market practices that draw on used materials. This insight also aligns well with [86], who find that highlighting a product's transformative potential increases rates of recycling waste materials. Notably, our focus on metamorphosis or transformation also distinguishes upcycling and recycling from conventional production modes that create products from raw materials that are mostly devoid of a prior life and identity (Study 6). Our results suggest that insights from recycling may generalize to upcycling and vice versa. For both practices, marketers would do well to utilize transformational product biographies as their unique selling proposition. This sets a first benchmark for marketers in upcycling and provides a new lens for thinking about recycling. To date, most recycled products stress the environmental benefits rather than the specialness affordance inherent to past identities. More broadly, our results answer calls for marketing research on products created in environmentally friendly ways ([48]) and add to findings that suggest that environmentally friendly options are not necessarily preferred or avoided for their environmental impact ([14]; [50]). We show that these products may be preferred if they afford specialness.

Highlighting a novel facet of storytelling within an upcoming mode of production provides substantial scope for future research. On the one hand, there are several open questions with regard to storytelling. One question is whether there are story elements that might be particularly powerful. In Study 3, we found that past identity salience triggered different narrative thoughts. Future research could explore how and whether such differences affect felt specialness and demand. Another open question is whether results would differ if marketers were to narrate the product's biography in full. Would the effect be enhanced because everyone picks up on the story, or would it be reduced because there is less scope for self-inferencing? A related question asks how powerful a product's biographical story would be compared with other stories told in marketing. This is a relevant but tricky question because stories are more than the sum of their elements. We nonetheless explored this in a supplementary study (Supplementary Study 2, Web Appendix W3). An ad for a laptop sleeve either implied its story through its stated past identity (upcycled from an old mosquito net) or narrated a story of its inception (the product creators were inspired by watching a spider web). The respective stories affected demand to the extent to which participants perceived the product to hold a story. Notably, the marketer-narrated story about the design inspiration imbued the product with less of a story and led to a smaller increase in appeal than the self-inferred story triggered by past identity salience. Future research is needed to identify whether the embodiment of a story in the product outperforms narrated stories that are meant to "rub off" onto the product.

Future research is also needed to ensure that the results are unaffected by the specific sampling frames we used. We tested our predictions across a range of samples from Austria, including Facebook users (Studies 1a–b), passers-by on campus (Study 2), convenience samples (Study 5), and pure student samples (Study 6). In addition, we conducted some studies with U.S.-based MTurk workers (Studies 3 and 4). Future research is needed to ensure that our conclusions extend beyond these populations. In particular, non-Western cultures may be more reluctant to adopt used goods ([89]).

Running our own pop-up store, we learned firsthand how different reactions to upcycling can be. Some people appear to be entirely averse to the notion of old source products. Past identity salience may intensify these individuals' aversions, but these individuals might never fall into the target group. This does, however, raise the question of whether brands should highlight the fact that their branded products become repurposed, and whether they should use the same or a different brand for repurposed products.

The effects we observed appeared rather robust regarding who makes the product (see follow-up analyses on perceptions as handmade for Study 4, Web Appendix W2) and what the product was made of. We surmise that the metamorphosis removes the potential taint of nearly any past identity, and we find in a supplementary study (Supplementary Study 3, Web Appendix W3) that even the salience of a truly disgusting past identity (dirty mosquito net) did not hurt demand. The appeal of the past identity may nonetheless influence the size of the effect. This speaks to the power of transformations in overcoming issues of contagion and opens up several lines of future inquiry. For example, does the extent to which the past identity becomes transfigured and distorted in the process of upcycling matter ([77])? Do functional source products (e.g., a glass window) result in different responses to hedonic source products (e.g., a decorative glass vase)? Moreover, who reacts most to past identity appeals, and are there people for whom such appeals backfire? Given that we find that demand is driven by felt specialness, it is plausible that customers who are low in power ([23]) or who feel a need for status ([24]; [75]) would react more strongly.

The observed power of a prior life may also advance research on other effects attributed to a product's origin or production mode. Storytelling principles might further increase the effectiveness of appeals related to prior users (e.g., [ 6]), brands, and production sites (e.g., [59]) as well as production and design modes (e.g., [29]). Whether results generalize to such diverse aspects of a product's origin is, however, an open question. Prior research has only tended to find positive effects of appeals to a product's origin when the highlighted origin aspect is desirable. This rests on the notion that the essence of what goes into a product persists in the final product. For example, [59] found that products made in a firm's original manufacturing location hold more appeal because they are believed to contain more of the brand's authentic essence. In contrast, we found that past identity appeals work even when they highlight a discarded origin. In this respect, our findings are particularly novel.

The salience of a product's past identity robustly increased demand across a wide variety of contexts (store and product level, online and offline), categories (e.g., wallets, vases, chandeliers), ad appeals, and product origins (e.g., mosquito nets, parachutes, airbags). In a real store (Study 2), revenues more than quadrupled when we made the past identities of products the focal point of our marketing materials. Online (Study 1a), social media likes more than doubled, and in all instances where we expected to find an increase in WTP or choice share, such an increase was found. The upcycled (vs. conventional) alternative was chosen at least 12% more often when the past identity was made salient.

Importantly, our results generalized to recycling. When marketing products made from either of those production modes—recycling or upcycling—marketers should appeal to the product's past, even though this past has no bearing on the product's present identity or functionality. Regardless of what source product we used, past identity salience never reduced demand. However, when upcycled products told their story at a glance, simply restating the past identity did not further boost demand. This finding holds two important implications. First, in recycling, products are broken down to their granular structure. The past identities of recycled products are thus never visually discernible, suggesting that the more prevalent practice of recycling may benefit from past identity salience even more consistently than upcycling. This is an important contribution. In line with our market observations, most companies that use recycled materials simply stress their environmental friendliness but do little to highlight the specific source material going into their products.

Second, the boundary condition of past identity discernibility (Study 5) may be overcome even for upcycled products. In two additional studies (Supplementary Studies 4a and 4b in Web Appendix W3), we devised ways in which marketers can do so. We found that easily executed visual tweaks that logically reinforce the product's biographical story boosted product demand even when the product's past identity was discernible at first glance. The key appears to lie in storytelling principles. These suggest reinforcing the chronological and causal structure inherent to the product's story ([51]; i.e., feature the past identity first and more prominently than the resulting product).

Our implications also extend to the general adoption of the practice of upcycling. Every year, two billion tons of waste go to landfills around the world, posing a continuous threat to the environment, the economy, and society ([77]; [84]). Encouraging upcycling means that product waste will be reduced, resulting in less landfill and incineration, more energy savings, and decreases in industrial emissions ([11]). Upcycling has experienced impressive growth rates ([72]) and allows for a value-adding possibility in companies' own waste management, but it is still a niche phenomenon. Entering this market may be a worthwhile opportunity. Whenever we asked participants to choose between an upcycled product and conventional product, a substantial proportion of people preferred the upcycled option, and our pop-up store sparked interest. The addition of upcycled products to a retailer's portfolio may be an actionable way to attract customers who want to feel special but lack the financial resources for status symbols.

Like Cinderella, the life of upcycled products holds the ingredients for the plot of a bestselling story. This article shows that we can learn from this story and that there may be more to storytelling than currently practiced in marketing. Stories truly unfold in customers' minds ([27]; [79]), but to date marketers appear to think that they have to do all the telling. Our results suggest that customers infer stories, even when they only see a single piece of information. Our results also suggest that stories may imbue potential weaknesses in a product's image (such as a useless past identity) with meaning that benefits rather than hurts demand. Customers appear to feel special when they obtain a product that allows them to infer its story. Perhaps it is time to think of marketing as the creation of a projection space for stories that customers tell and help marketers sell.

Supplemental Material, DS_10.1177_0022242919872156 - A Cinderella Story: How Past Identity Salience Boosts Demand for Repurposed Products

Supplemental Material, DS_10.1177_0022242919872156 for A Cinderella Story: How Past Identity Salience Boosts Demand for Repurposed Products by Bernadette Kamleitner, Carina Thürridl and Brett A.S. Martin in Journal of Marketing

1 Author ContributionsThe first two authors contributed equally to this work.

2 Associate EditorAric Rindfleisch

3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge funding by QUT Business School for Studies 1a and 1b.

5 Online supplement: https://doi.org/10.1177/0022242919872156

6 1We thank an anonymous reviewer for this suggestion and present details of these analyses in Web Appendix W3.

7 2In replication studies (see Web Appendix W4) we generalized these effects to other products, tested for another potential alternative explanation (perceived quality), and controlled for general upcycling affinity.

8 3For evidence that the results generalize to other product categories, see a comprehensive replication study in Web Appendix W4.

9 4We thank the anonymous review team for these suggestions.

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By Bernadette Kamleitner; Carina Thürridl and Brett A.S. Martin

Reported by Author; Author; Author

A Commentary on “Transformative Marketing: The Next 20 Years” 

Transformative: Causing a marked change in something. Kumar’s (2018) insightful editorial focuses on transformative marketing and its implications for research in marketing, marketing practice, marketing education, and marketing and public policy. While Kumar covers a vast terrain, in this commentary, I specifically focus on the transformative marketing landscape framework and related discussion in the editorial. In the framework, Kumar delineates a number of environmental changes that challenge the status quo of organizations, transformative marketing as the organizational response, and greater marketing effectiveness and efficiency as among the outcomes. Kumar notes that transformative marketing operates in an environment that is rooted in data (i.e., use of a firm’s data resources to formulate and implement marketing strategy) and innovation. He notes that unlocking the potential of a firm’s data resources, in itself, constitutes an innovation. Kumar further notes that firms operating in a data-rich environment (a consequence of the rapid growth of digital data and digitization of data) actively engage in new ways of exploring data to gain insights.

As a complement to the transformative marketing framework presented in Kumar (2018), in this commentary, I propose a conceptual framework delineating the relationships between a firm’s customer information assets, information analysis capabilities, customer knowledge, marketing strategy, and performance in a transformative landscape. Common to both frameworks are the outcomes of marketing strategy effectiveness and efficiency. Data resources, unlocking their potential, and generating insights from them in Kumar’s framework overlap with customer information assets, information analysis capabilities, and customer knowledge, respectively, in the proposed framework. The principal focus of the proposed framework is the digital data-rich environment as a transformative force in marketing. Although Kumar does not explicitly delineate this aspect in his framework, it reverberates in much of his discussion relating to it.

Srivastava, Shervani, and Fahey (1998) conceptualize marketbased assets as resources that arise from a firm’s commingling with entities in the external environment. They broadly distinguish between relational assets (resources that arise from a firm’s relationship with key external stakeholders) and intellectual assets (knowledge gained by the firm about the environment and specific entities in the environment). The authors highlight brand equity, customer equity, and channel equity as illustrative of a firm’s relational market-based resources, and knowledge about market conditions, customers, competitors, channel members, and suppliers as illustrative of intellectual market-based resources.

In a digital data-rich environment, three market-based resources of major importance are a firm’s customer information assets, customer information analysis capabilities, and customer knowledge. Figure 1 presents a conceptual framework delineating the relationships between these resources, marketing strategy, and performance. As the figure shows, by effectively leveraging its customer information assets and information analysis capabilities, a firm can achieve an advantageous resource position in customer knowledge. Analogously, by effectively leveraging its advantageous resource position in customer knowledge to inform its marketing strategy decisions, a firm can achieve a better marketing strategy–market environment fit relative to its competitors and, thereby, greater marketing strategy effectiveness and efficiency as well as superior marketing and financial performance.

Customer information assets is defined as information of economic value about customers owned by a firm. In the DataInformation-Knowledge-Wisdom Pyramid framework (BurtonJones 1999, p. 6), information is conceptualized as “processed data that is meaningful and of value,” and knowledge as “understanding of information and its associated pattern.” Customer information analysis capabilities is defined as a firm’s complex bundles of skills and knowledge embedded in its organizational processes that it uses to generate customer knowledge from its customer information assets. The proposed definition builds on Day’s (1994) conceptualization of capability as complex bundles of skills and knowledge embedded in a firm’s organizational processes by which it transforms available resources into valuable outputs. It conceptually overlaps with Rubera, Chandrasekaran, and Ordanini’s (2016, p. 170) conceptualization of market information management capabilities as skills used by a firm to develop and use market knowledge (i.e., customer knowledge and competitor knowledge).

Customer knowledge is defined as a firm’s understanding of customers that informs its business decisions. Customer knowledge, broadly construed, encompasses a firm’s insights into or understanding of customers’ attitudes, behaviors, beliefs, demographics, desires, emotions, habits, interests, involvement, lifestyles, motives, needs, perceptions, preferences, psychographics, tastes, values, wants, and more—hence the proposed inclusive conceptual definition. However, the broad scope of customer knowledge necessitates the scope of the operational definition to be limited to a specific facet (e.g., wants) or a small number of facets (e.g., affects, cognitions, behaviors) of customer knowledge in specific research studies.

Marketing strategy is defined as a business’ crucial decisions concerning its planned pattern of behavior in the marketplace to achieve (facilitate the achievement of) a competitive advantage, and thereby realize specific organizational objectives. Marketing strategy addresses the fundamental question of how to compete in the marketplace (How to create, communicate, and deliver products that offer value to customers in mutually beneficial exchanges with the organization? How to engender specific affects, cognitions and behaviors in target customers?) Crucial and internally consistent decisions that are germane to marketing strategy include marketing activities to perform, their manner of performance in chosen markets and market segments, and the allocation of marketing resources among markets, market segments, and marketing activities (see Varadarajan 2010). In the proposed framework, the focus is not the full spectrum of marketing strategy, but rather those elements of marketing strategy that can be optimized in a digital data-rich environment (e.g., personalization of product offering and/or specific elements of the marketing mix).

Fit refers to the degree to which a firm’s marketing strategy is appropriate for a particular market environment. In the context of a digital data-rich environment, in the limit, fit is the degree to which specific elements of a firm’s targeted marketing efforts are appropriate for individual customers (e.g., personalization of the product offering and promotional message content). Marketing strategy effectiveness is the extent to which the implementation of the strategy enables an organization to achieve its stated objectives. Marketing strategy efficiency is the ratio of the amount of marketing output generated to the amount of marketing resource inputs employed to implement the strategy.

In the internet-enabled, interactive and digital data-rich environment, the effect of customer knowledge advantage on marketing performance (i.e., effect of increase in market share in a growth market on the size of a firm’s customer base) will have a positive feedback effect on customer information assets accumulation. This is shown in the framework by the link from marketing performance to customer information assets. The dotted arrows in Figure 1 denote a firm’s assessment of gaps in its customer knowledge that limit its ability to compete more effectively and efficiently in the marketplace, and the underlying gaps in customer information assets and customer information analysis capabilities. Along the lines of heterogeneity among competitors in their customer information assets, customer information analysis capabilities and customer knowledge, there will also be heterogeneity among them with respect to customer knowledge gaps they aware of (known unknowns), and gaps they are unaware of (unknown unknowns).

The relationships delineated in the framework (Figure 1) are informed by insights from the resource-based view (RBV), capabilities-based view (CBV), and dynamic capabilities-based view (DCBV) of the firm. RBV posits that heterogeneous market positions result from effectively leveraging heterogeneous firm resources that are valuable, rare, inimitable and nonsubstitutable (VRIN resources) to achieve and sustain a competitive advantage in the marketplace (Barney 1991). CBV distinguishes between resources (stocks of available factors controlled by the firm), and capabilities (capacity of a firm to deploy resources, usually in combination, using organizational processes to achieve a desired end) (Amit and Schoemaker 1993). Rather than the mere possession of idiosyncratic assets and static capabilities, the DCBV considers a firm’s ability to integrate, build, and reconfigure competencies in response to a changing environment as crucial to achieving and sustaining a competitive advantage in the marketplace (Teece, Pisano, and Shuen 1997). Teece, Pisano, and Shuen (p. 516) define dynamic capabilities as “the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments.” The knowledge-based view (KBV) posits that knowledge is at the core of a firm’s competitiveness, and the way a firm creates, acquires, assimilates, and exploits knowledge leads to the creation of new sources of revenues and persistence of performance differences among competing firms (Grant 1996; Rubera, Chandrasekaran, and Ordanini 2016).

In the proposed framework (Figure 1), customer knowledge, modeled as a firm resource, is distinct from knowledge embedded in a firm’s customer information analysis capabilities. The former refers to knowledge as understanding (knowing what something means) and the latter to knowledge as knowhow (knowing how to do something) (see Kogut and Zander 1992). Bierly, Kessler, and Christensen’s (2000) conceptualization of knowledge as the understanding of information and its associated pattern augurs with the former. Organizational knowledge embedded in a firm’s capabilities refers to the latter. Its tacit nature makes it relatively difficult for competitors to diagnose and/or replicate (Teece, Pisano, and Shuen 1997) and transfer from one firm to another without also transferring ownership of the firm, or some self-contained subunit of the firm within which the capabilities reside (Makadok 2001).

Although the proposed framework builds on literature insights from the RBV, CBV, and DCBV of the firm, it differs in certain respects. The proposed framework highlights organizational contexts in which certain types of resources (assets and capabilities) are leveraged to create another type of resource (knowledge), which is then leveraged to effectively compete in the marketplace. The following linkages underlying the RBV,

CBV, and DCBV and the proposed framework (Figure 1) serve to highlight this distinction:

• RBV: Competing by leveraging superior resource positions in VRIN resources (A) → Competitive Advantage (B) → Performance (C).

• CBV and DCBV: Competing by leveraging superior firm

capabilities (A2) to effectively deploy superior firm resources (A1) → Competitive Advantage (B) → Performance (C).

• Assets, capabilities, and knowledge framework (Figure 1):

Utilizing superior firm capabilities (A2) to effectively leverage superior firm assets (A1) to create superior knowledge resources (A3) → Competing by leveraging superior knowledge resources (A3) → Competitive Advantage (B) → Performance (C).

In the broader context of interdependencies between three broad types of firm resources (information assets, information analysis capabilities, and knowledge), the proposed framework delineates the relationships between a firm’s customer information assets, customer information analysis capabilities, customer knowledge, marketing strategy and performance. The representative assets listed in Boxes A1 and A2 in Figure 1 highlight these relationships in the context of the digital data-rich environment characteristic of the transformative marketing landscape. The principal relationships delineated in Figure 1 can formally be stated as follows:

Customer Knowledge Advantage Hypothesis

Heterogeneity in customer information assets and customer information analysis capabilities, through their effects on customer knowledge, result in heterogeneity in the competitive market positions of firms. This effect will be more pronounced in digital data-rich competitive environments.

The following real-world examples provide anecdotal support for the proposed framework and hypothesis.

Doubling down on your distinctive strengths not only helps you fight disruption from upstarts, but it also enables you to disrupt an industry on your own terms. That’s exactly what Netflix did. In the late 1990s, the company competed directly with the Blockbuster retail chain. In 2007, when streaming video became viable, Netflix rapidly pivoted to offer that service. It … has pioneered the use of artificial intelligence and machine learning to tailor its output to consumer interests. All along, its success has been enabled by a core distinctive strength: the ability to understand what its customers want and do, using in-depth analytics and the behavioral data it captures. (strategy+business 2017)

Another blowout quarter from Alibaba highlights the Chinese e-commerce company’s ability to harness its trove of data to boost earnings… Its operating margin widened by 7 percentage points, which management attributed in part to better use of data, as operating profit almost doubled from a year ago…. It attributes the revenue increase to more shoppers as well as its ability to deliver more relevant content to them.… The company accounts for around three-quarters of online retail sales in China and hence has a trove of data on consumer behavior. Such data allow Alibaba to display ads to shoppers that they will most likely be interested in… Merchants are willing to pay higher prices if they know the ads are likely to draw in sales. Better algorithms allow Alibaba to earn more ad dollars without a similar rise in costs. (Wong 2017, p. B12)

By their very nature, models and frameworks such as that proposed here (Figure 1) are abstractions of reality. In this regard, an important societal issue in the current digital data-rich environment (and the focus of numerous journal articles, opinion pieces, and editorials) that merits mention is the divide between a firm profitably serving consumers by leveraging its advantageous resource positions in customer information assets, information analysis capabilities and customer knowledge, and invasion of consumers’ privacy.

Certain environmental developments have a transformative effect on marketing, and certain marketing innovations have a transformative effect on the environment. The former is the focus of this commentary—transformative effects of a digital data-rich environment on marketing. The scope of Kumar’s (2018) transcends both, but with a greater emphasis on the former. The effects of transformative innovations, broadly construed (e.g., breakthrough, disruptive, game changing, radical, revolutionary innovations), such as the following, provide a baseline frame of reference for gaining insights into the transformative effects of marketing innovations, both past (e.g., supermarket, discount, and department store chains; e-commerce, mobile e-commerce, and e-customer relationship management) and future:

• Major changes in how a specific need or set of needs and

wants of consumers are met.

• Major changes in how consumers search, evaluate, purchase, consume/use, and dispose of a product—fundamental changes in consumer behavior.

• Major changes in the value proposition offered to customers and/or expected by customers.

• Major changes in how a product is produced, promoted, distributed, priced, and so on—fundamental changes in the marketing behavior of firms.

• Emergence of a new industry, or convergence of two or more existing industries.

• Major changes in the structural characteristics of an industry such as market size, market growth rate, industry concentration, entry barriers and exit barriers.

• Major changes in the composition of competitors in an industry following entry of new firms, exit of incumbent firms, and changes in the competitive positions of incumbent firms.

• Major changes in the configuration of value chain activities, assets mix, cost elements, and/or revenue model in an industry.

• In the limit, the effects of an innovation (e.g., civilization transforming/epochal innovation) can be tectonic in nature, such as transformative cultural, economic, political and/or social changes.

The history of successful transformative innovations, the technologies underlying them, and the role of marketing as a catalyst in their adoption and diffusion is suggestive of technology, innovation, and marketing as a transformative triad. The dawn of a digital data-rich environment portends technology, innovation, data, and marketing as a transformative quartet.

a The customer information assets and customer information analysis capabilities that are shown are representative of a larger number of assets and capabilities, respectively.

b The direct link from marketing strategy to financial performance denotes the effect of marketing strategy efficiency on financial performance.

c The link from marketing performance to customer information assets denotes the effect of increase in market share in a growth market on the size of a business’ customer base.

Notes: The framework focuses on a specific facet of the business landscape that is transformative of marketing—an Internet enabled, interactive, digital data-rich market environment. Of the various types of firm resources, the framework specifically focuses on three major types: assets (A1), capabilities (A2), and knowledge (A3). The dotted arrows denote a firm’s assessment of customer knowledge gaps that constrain its ability to more effectively and efficiently compete in the marketplace, the underlying customer information assets gaps, and customer information analysis capabilities gaps. Here again, there will be heterogeneity across firms in respect of known unknowns (customer-related knowledge gaps that the firm is aware of), and unknown unknowns (customer-related knowledge gaps that the firm is not even aware of).

DIAGRAM: FIGURE 1 Conceptual Framework

1 Amit, Raphael, and Paul J.H. Schoemaker (1993), “Strategic Assets and Organizational Rent,” Strategic Management Journal, 14 (1), 33–46.

2 Barney, Jay (1991), “Firm Resources and Sustained Competitive Advantage,” Journal of Management, 17 (1), 99–120.

3 Bierly, Paul E., Eric H. Kessler, and Edward W. Christensen (2000), “Organizational Learning, Knowledge and Wisdom,” Journal of Organizational Change Management, 13 (6), 595–618.

4 Burton-Jones, Alan (1999), Knowledge Capitalism: Business, Work and Learning in the New Economy. Oxford: Oxford University Press.

5 Day, George S. (1994), “The Capabilities of Market-Driven Organizations,” Journal of Marketing, 58 (4), 37–52.

6 Grant, Robert M. (1996), “Toward a Knowledge-Based Theory of the Firm,” Strategic Management Journal, 17 (1), 109–22.

7 Kogut, Bruce, and Udo Zander (1992), “Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology,” Organization Science, 3 (3), 383–97.

8 Kumar, V. (2018), “Transformative Marketing: The Next 20 Years,” Journal of Marketing, 82 (4), 1–12.

9 Makadok, Richard (2001), “Toward a Synthesis of the Resource-Based and Dynamic-Capability Views of Rent Creation,” Strategic Management Journal, 22 (5), 387–401.

Rubera, Gaia, Deepa Chandrasekaran, and Andrea Ordanini (2016), “Open Innovation, Product Portfolio Innovativeness and Firm Performance: The Dual Role of New Product Development Capabilities,” Journal of the Academy of Marketing Science, 44 (2), 166-84.

Srivastava, Rajendra K., Tassaduq A. Shervani, and Liam Fahey (1998), “Market-Based Assets and Shareholder Value: A Framework for Analysis,” Journal of Marketing, 62 (1), 2-18.

strategy+business (2017), “Disruptors and the Disrupted: A Tale of Eight Companies-in Pictures,” (September 27), https://www.strategy-business.com/pictures/Disruptors-and-the-DisruptedA-Tale-of-Eight-Companies-in-Pictures?gko=dd95b.

Teece, David J., Gary Pisano, and Amy Shuen (1997), “Dynamic Capabilities and Strategic Management,” Strategic Management Journal, 18 (7), 509-33.

Varadarajan, Rajan (2010), “Strategic Marketing and Marketing Strategy: Conceptual Domain, Definition, Fundamental Issues and Foundational Premises,” Journal of the Academy of Marketing Science, 38 (April), 119-40.

Wong, Jacky (2017), “Alibaba Cashes in on Data Trove,” The Wall Street Journal (August 18), B12.

A Meta-Analysis of When and How Advertising Creativity Works 

Although creativity is often considered a key success factor in advertising, the marketing literature lacks a systematic empirical account of when and how advertising creativity works. The authors use a meta-analysis to synthesize the literature on advertising creativity and test different theoretical explanations for its effects. The analysis covers 93 data sets taken from 67 papers that provide 878 effect sizes. The results show robust positive effects but also highlight the importance of considering both originality and appropriateness when investing in advertising creativity. Moderation analyses show that the effects of advertising creativity are stronger for high- (vs. low-) involvement products, and that the effects on ad (but not brand) reactions are marginally stronger for unfamiliar brands. An empirical test of theoretical mechanisms shows that affect transfer, processing, and signaling jointly explain these effects, and that originality mainly leads to affect transfer, whereas appropriateness leads to signaling. The authors also call for further research connecting advertising creativity with sales and studying its effects in digital contexts.

Keywords: advertising; affect transfer; creativity; meta-analysis; processing; signaling

Creativity is important in marketing and is often considered to be at the heart of the advertising industry. The importance of creativity is highlighted, for example, in the popularity of industry competitions, such as the Cannes Lions International Festival of Creativity, and the growing academic literature on its effects (e.g., [68]; [85]). However, the value of creativity is also subject to longstanding debate ([ 5]; [52]), and recent reports highlight that marketers are increasingly growing skeptical of advertising creativity ([63]; [66]) and decreasing their investments in it ([27]).

When and how is advertising creativity most valuable? Marketers wanting to answer these questions will find little guidance in the academic literature. Although the link between advertising expenditure and advertising effects has been consistently supported ([38]; [80]), to date, there is no comprehensive account of advertising creativity and its influence on consumer response. Even [83] failed to account for creativity as a factor in their insightful and influential review of how advertising works.

Several factors seem to hold back scholarship in advertising creativity: ( 1) contrasting empirical results on its effects in terms of ad and brand outcomes (e.g., [51]; [77]; [82]), ( 2) disagreements over what creativity is and how it should be assessed (e.g., [60]; [77]), ( 3) limited understanding of moderators of its effect (e.g., [86]), and ( 4) ambiguity about the kind of theories that best explain how creativity works (e.g., [85]; [86]). Given the apparent confusion about what advertising creativity is and when it might benefit a brand, it is not surprising that marketers often make the wrong decisions when investing in advertising creativity ([68]).

In this article, we synthesize the fragmented literature on consumer response to advertising creativity. Based on a literature review, we develop a conceptual framework linking advertising creativity to consumer outcome responses in terms of ad, brand, and sales. Through a meta-analysis, we then integrate 878 effect sizes in the first quantitative empirical overview of the literature. Thus, we capture the impact of advertising creativity on 19 different consumer responses taken from 93 data sets in 67 papers. We thereby contribute a comprehensive and empirically grounded account of how and when advertising creativity works, providing researchers with generalized findings that can serve as benchmarks and a common foundation for future studies of this important topic.

First, we provide an empirically validated account of how advertising creativity works. The results show robust positive effects of advertising creativity on consumer responses but also inform researchers about the relative importance of various consumer responses to advertising creativity. Overall, effects are stronger for ad rather than brand responses (r =.491 vs..317) and for attitudinal rather than memory outcomes (all below.140). This suggests that the main advantages of advertising creativity are not grabbing attention and making ads memorable but rather the ability to foster positive ad and brand attitudes.

Second, we highlight that advertising creativity is different from originality. Effects on consumer response are greater when creativity is measured as a bipartite construct comprising of originality and appropriateness. Effects of originality only on ad and brand attitudes are comparatively small (.362 and.164), suggesting that marketers who view creativity as synonymous with originality will not reap the full benefits of investments in advertising creativity.

Third, we show that the different theoretical accounts used in the literature to explain how advertising creativity works are complementary. Although previously considered separately, affect transfer, processing, and signaling provide the best account when considered jointly. The results further show that processing is a key mediator of the effects, whereas originality fosters affect transfer and appropriateness signaling. When marketers invest in bipartite creativity, affect transfer and signaling occur in parallel to processing, which can explain the stronger effects of creativity compared with originality only.

Fourth, we find that when the three theoretical accounts are considered jointly, the effects of advertising creativity on brand response are not dependent on ad responses. This is in line with the signaling account of advertising creativity and suggests that to fully understand how advertising creativity works, marketers should assess consumer responses in terms of brand rather than ad outcomes.

For managers, the results provide guidance on how, when, and why to invest in advertising creativity. For example, advertising creativity leads to greater ad responses in high (vs. low) involvement communication contexts (.653 vs..340) and (marginally) for unfamiliar (vs. familiar) brands (.577 vs..435). The literature review also highlights the need for more studies on the relationship between advertising creativity and sales, as well as its effects in digital media contexts, two areas that are especially important given the ongoing debate about the value of advertising creativity in contemporary marketing practice.

Figure 1 presents the conceptual framework guiding the meta-analysis. We developed this framework on the basis of a review of the literature on consumer responses to advertising creativity. The framework focuses on consumer responses that have been empirically studied and distinguishes between immediate and outcome responses. The categorization of immediate responses is based on the three main theoretical accounts of how advertising creativity works found in the literature: affect transfer, processing, and signaling (i.e., consumer response in terms of affect, processing, and perceived signals at the time of exposure to creative advertising). The categorization of outcome variables is based on ad and brand responses and characterized in terms of attitudinal or memory responses (i.e., longer-lasting responses related to the ad and brand, such as attitudes, memory effects, and sales). The framework also highlights two moderators of these responses: definitions and assessments of advertising creativity and properties of the communication context (involvement and familiarity).

Graph: Figure 1. How and when advertising creativity works: conceptual framework.aNot tested empirically due to lack of data.

Whereas research on advertising creativity generally has found positive effects on immediate and outcome responses (for reviews, see [74]] and [77]]), empirical findings suggest that the effects vary between different types of consumer responses. Findings generally show that advertising creativity has benefits in terms of immediate responses, such as attention ([65]; [78]), positive affect ([30]; [86]), and signals, such as perceived sender effort ([19]; [50]), but results are inconsistent regarding when and how this might also lead to outcome responses, such as attitudes and intentions ([61]; [77]) or memory effects ([65]; [82]). Results also varied regarding whether attitude and memory outcomes are affected ([ 4]). In line with the overall literature, we hypothesize that advertising creativity has positive effects on immediate and outcome responses:

• H1: Advertising creativity has positive effects on (a) immediate responses and (b) outcome responses.

However, from a managerial perspective, understanding whether investments in advertising creativity mainly affect ad rather than brand response, and whether advertising creativity is better at stimulating attitude or memory outcomes, is also important. Given the inconsistencies in the literature, we qualify this hypothesis by asking what type of consumer responses are most affected:

• RQ1: Is consumer response stronger for ad versus brand outcomes?
• RQ2: Is consumer response stronger for memory versus attitude outcomes?

As indicated in the introduction, a key challenge in the literature is the different views on advertising creativity. Creativity is a general construct that has been widely researched in fields such as psychology and organizational behavior, as well as in marketing ([37]; [74]). Creativity can be used to describe individuals (e.g., an art director at an advertising agency), processes (e.g., design thinking methods used to brainstorm advertising campaigns), or outputs (e.g., the actual ad executions used in a marketing campaign). In this article, we adopt the output perspective.

Drawing on the creativity literature ([ 2]; [33]; [73]), we define advertising creativity as advertising execution(s) that is (are) original and appropriate. This bipartite definition of creativity has been widely adapted in the marketing literature, in which the definition has been applied in advertising ([14]; [41]), new product development ([ 8]; [37]), and consumer behavior ([ 9]; [62]). As argued by [ 2], a bipartite definition of creativity is required, because outputs that are original or unique but carry no use or meaning are perceived as weird or bizarre. However, any judgments of creativity are subjective and likely to vary across context and time. For example, judgments about originality and appropriateness in a work of art differ from the same judgments in an advertising context (even if the actual object being judged is the same). Similarly, judgments of the creativity of the same object can vary over time. In the art domain, for example, there are several artists who are now considered creative but whose art was controversial or even rejected in their time (e.g., Monet, Picasso, Dalí). Such works were initially seen as "weird" or "bizarre," mainly because the type of appropriateness expected of them at the time of creation was a literal representation of reality. Thus, these artists were redefined as creative later, when judgment of appropriateness changed.

In the advertising context, the best documented dimension of creativity is originality. This dimension has also been referred to as novelty, divergence, unexpectedness, and newness ([42]; [48]; [76]; [78]). Originality has positive effects on consumer responses to advertising, as originality makes advertising more likely to be attended and processed ([65]; [77]). Originality also has a positive effect on consumer response, as people have a predisposition to appreciate divergent stimuli and deem them intrinsically interesting ([86]). Advertising practitioners typically view originality as the most defining aspect of advertising creativity ([48]; [60]), especially when it comes to advertising creativity awards ([15]; [78]). Thus, it is not surprising that many scholars focus primarily or exclusively on originality when assessing advertising creativity ([49]; [65]).

When it comes to appropriateness, this dimension complements originality by connecting the advertisement with brand strategy and consumer problem-solving abilities and goals ([ 3]; [25]; [60]; [78]). In the advertising literature, appropriateness is also referred to as relevance and usefulness (and when assessed by practitioners as "on strategy"; cf. [41]; [74]). Appropriateness as such has received much research attention (often using the term "relevance"; e.g., [31]). However, in contrast with originality, scholars rarely consider appropriateness to be an indicator of creativity in and of itself. Instead, researchers typically view appropriateness as a prerequisite for advertising to be interesting to its intended audience regardless of its level of creativity.

Theoretically, it is clear that creativity is both originality and appropriateness. Some scholars also argue that additional dimensions could be needed to fully understand advertising creativity ([ 3]; [30]). They argue in favor of including a third advertising-specific dimension of creativity—namely, the quality of the ad execution, also referred to as artistry or production ([60]; [78]). In the literature, we distinguish four approaches to empirically assess advertising creativity. First, some studies measure advertising creativity as a holistic perception of the creativity of an ad, typically by using a single item "creative" or multiple creativity items that do not refer specifically to different dimensions (e.g., [71]). Second, other studies rely on only one dimension of advertising creativity (typically originality; e.g., [57]). Third, acknowledging the bipartite definition of creativity, some researchers use the interaction between originality and appropriateness as a creativity measure (e.g., [78]). Fourth, acknowledging the multidimensionality of the bipartite definition, some studies rely on measures of both originality and appropriateness ([42]), sometimes combined with one or more additional dimensions related to the production quality or artistic value ([60]; [68]).

We argue that researchers who focus on originality only (e.g., [57]; [65]) are likely to get different results from those who study creativity in terms of a bipartite (e.g., [60]; [78]). Although we cannot test the validity of different assessments, we propose that the best measure of advertising creativity should explain more variance in outcome response variables than alternative measures of creativity, leading to stronger effect sizes (for a similar argument, see [23]). The approach that has the highest explanatory value should also be the most managerially relevant. Given that creativity is defined as a combination of originality and appropriateness, we propose that the effect sizes should be stronger when both dimensions are considered and weaker when only originality is used. Thus,

• H2: The effect of advertising creativity on (a) ad response and (b) brand response is stronger when creativity is assessed as a bipartite versus as originality only.

Although we expect advertising creativity to generally have positive effects on consumer response (H₁), we also expect properties of the communication context to moderate these effects. In the literature review, it was apparent that little attention has been paid to such contextual moderators in the existing literature ([86]). However, we identified two theoretically relevant moderators: involvement and familiarity. Both variables have been found to affect consumer response to advertising in general, but of interest here is how they affect consumer responses to advertising creativity. Specifically, the literature suggests that advertising creativity (i.e., a combination of originality and appropriateness) has benefits regardless of the type of processing (peripheral vs. central; e.g., [10]) depending on these moderators.

Consumers' involvement with advertising reflects their level of interest in brand evaluation in any given context and has been found to moderate the effects of advertising processing and response (e.g., [56]; [59]). Specifically, consumer responses to advertising differ depending on how much effort goes into processing it. For example, high involvement with a product category motivates consumers to pay attention to and actively process advertising. When involvement is low, attention is typically allocated to other things, and consequently, ad processing is limited and utilizes few processing resources and peripheral cues (e.g., [10]; [56]).

Although advertising creativity has typically been thought of as an attention-grabbing device (e.g., [65]), implying that it would work best in low-involvement contexts (where it can foster situational involvement; [10]), creativity has been found to have additional processing advantages when it comes to high-involvement contexts ([79]; [86]). In a low-involvement context, any additional processing stimulated by a creative ad is likely to be shallow ([56]; [86]). In a high-involvement context, however, additional processing makes consumers more likely to actively assess the claims. In this context, the combination of originality and appropriateness fosters more open-minded and less defensive processing of claims made ("willingness to delay closure"; [39]; [86]). This means that consumers will be more open to new information about the brand and less likely to use defensive mechanisms when processing advertising messages that are communicated creatively. Whereas advertising creativity in low-involvement contexts stimulates more affective processing, in high-involvement contexts creativity influences affective and cognitive processing ([86]). In both contexts, advertising creativity should have a positive impact on consumer response, but in a high-involvement context, in-depth processing, coupled with the willingness to delay closure, makes the effects stronger. Thus,

• H3: The effect of advertising creativity on (a) ad response and (b) brand response is stronger for high-involvement versus low-involvement products.

Familiarity reflects the extent of consumers' direct and indirect experience with a stimulus, such as a product or a brand ([ 1]; [11]). Consumer response to advertising has been found to vary with familiarity ([55]; [75]). Specifically, the effects of advertising are generally stronger for unfamiliar than familiar brands. This effect is due to consumers not being able to draw from previous experiences (neither their own nor the experiences of others) when evaluating unfamiliar brands, which makes advertising more important for these brands. However, advertising for unfamiliar brands wears out faster ([11]). For marketers of unfamiliar brands, this poses a challenge, as they need advertising to build familiarity but also must be careful about how they advertise to avoid negative reactions caused by (too much) repetition.

Familiarity has also been found to moderate the effects of advertising creativity in terms of familiarity with the specific ad ([14]; [65]). [14] found that advertising creativity has two main benefits when it comes to repetition: ( 1) it generates more positive effects upon initial exposure, and ( 2) it resists wear-out over multiple exposures. The latter finding is in line with results showing that advertising creativity (in terms of originality) helps draw attention to familiar ads that might otherwise be overlooked due to tedium ([65]). For unfamiliar brands, these advantages are more important ([11]). Overall, this suggests that advertising creativity should be beneficial for unfamiliar and familiar brands, but given its attention-grabbing character ([65]), the immediate wear-in effect that it can generate ([14]), and the higher impact of advertising in general ([55]; [75]), the effects should be stronger for unfamiliar brands. Thus,

• H4: The effect of advertising creativity on (a) ad response and (b) brand response is stronger for unfamiliar versus familiar brands.

In the literature, scholars have used three main theories to explain the effects of advertising creativity on consumer responses. These accounts focus on different immediate responses as key mediators of the effects of advertising creativity on outcome responses. The affect transfer model focuses on the potential of creativity to evoke positive feelings that spill over into consumer responses to the ad and brand (i.e., regarding "positive affect" as a key mediator; [86]). The processing model focuses primarily on the ability of creativity to get consumers interested in the ad and brand (i.e., "ad processing" is the key mediator; [78]). The signaling model proposes that creativity works as a marketing signal, directly influencing perceptions about the sender and thus, consumer responses to the brand (i.e., "perceived sender effort" is the key mediator; [19]).[ 5]

A common explanation for the effects of advertising creativity is based on affect transfer ([20]; also referred to as affect infusion; [26]). This explanation focuses on the ability of affectively loaded information to transfer into other, more or less unrelated, targets. In the advertising creativity context, the affect transfer model builds on the fact that consumers generally like creative ads ([71]; [77]). Processing creative ads is seen as intrinsically motivating and pleasurable ([71]; [86]), which means that consumers are likely to experience positive affect while exposed to such advertising. This positive affect spills over to the ad and brand, leading them to be evaluated more favorably ([30]; [86]). According to this explanation, the positive effects are driven by creative ads being more enjoyable and liked, and the positive feelings that this stimulates. Although theoretically exceptions might occur, as may be the case for executions of fear appeal advertising that combines originality and appropriateness, the reviewed literature on advertising creativity unanimously provides empirical support for positive reactions to advertising creativity. Thus,

• H5: The effect of advertising creativity on (a) ad response and (b) brand response is mediated by positive affect.

Explanations of how advertising works often draw on information processing models (e.g., [56]; [59]). Specifically, they explain consumer responses to advertising based on different levels of ad processing. This is also a common explanation for the effects of advertising creativity. Creativity is said to stand out, thus making creative ads more likely to be attended to and processed ([78]; [86]). This means that more creative advertising stimulates more ad processing, resulting in longer exposure and greater attention ([30]; [65]), which has positive effects on consumer outcome response. According to this explanation, the positive effects of creativity are driven by creativity being more interesting and therefore, processed more. Thus,

• H6: The effect of advertising creativity on (a) ad response and (b) brand response is mediated by ad processing.

A third explanation for the effects of advertising creativity focuses on creativity as a signal of brand and company ability (e.g., [19]; [50]). This model builds on research on marketing signals ([44]; [46]), showing that certain behaviors on behalf of a firm (e.g., offering long-lasting warranties) can be used to signal unobservable quality to consumers. For example, advertising spending (i.e., monetary investments) has been found to work as a signal of brand quality ([45]; [47]). Similarly, advertising creativity has been found to be perceived by consumers as a signal that the sender has invested effort (in terms of time and money) in their brand ([18]; [19]). Through creative advertising, a brand conveys that it is committed to its advertising and its products, which is interpreted as a signal that positively affects how the brand is perceived and evaluated. According to this explanation, the positive effects of creativity are driven by creativity signaling effort on behalf of the sender, thus affecting the ad and brand positively. In contrast to the other two models, this account considers process reactions to creative advertising to be about the immediate perceptions of the brand rather than the ad. Thus,

• H7: The effect of advertising creativity on (a) ad response and (b) brand response is mediated by perceived sender effort.

Although the three theoretical accounts typically have been used in isolation (for an exception, see [86]]), combining the three models should provide a more comprehensive account of how advertising creativity works. Furthermore, the omission of any one of the intermediary effects might lead to the overestimation of the other ([83]). Therefore,

• H8: A model of advertising creativity that considers (a) positive affect, (b) ad processing, and (c) perceived sender effort jointly better explains the effects of advertising creativity on ad and brand responses than any of the three models used separately.

To test this hypothesis, we propose a full model, which incorporates all three theoretical mechanisms (see Figure 2). Given that the initial models focus on different variables, we propose several additional relationships in the full model. First, ad processing is likely to spur stronger perceptions of sender effort. This is because processing facilitates a more careful understanding of the ad ([77]), which should, in turn, enhance perceptions of sender effort stimulated by advertising creativity. Second, affective responses can influence not only the ad and brand but also perceptions of sender effort. The underlying logic is, again, affect transfer ([86]). Furthermore, affect and processing should be positively related, because feelings ease processing (mood theory), and easy processing is experienced as a good feeling (processing fluency theory).

Graph: Figure 2. How advertising creativity works: Full model including estimation results (standardized path coefficients).†p <.10.*p <.05.**p <.01.***p <.001.Notes: Dotted lines indicate paths that were added to the combined model to form the full model.

For this meta-analysis, we selected papers that provide estimates of the effects of advertising creativity on various consumer responses. According to our bipartite definition of advertising creativity, advertising creativity comprises originality and appropriateness. To be able to assess the relevance of different assessments of creativity, we included all studies that identify as "ad* creativity" studies independent of their definition and operationalization of advertising creativity. This means that we also included all studies that relied on advertising stimuli judged to be creative (even if they did not use the bipartite definition), as well as studies that investigated the two main dimensions of creativity, even if they did not use the term "creativity" ([51]).

To identify relevant papers, we first referred to review articles that provide an overview of previous research on advertising creativity (e.g., [74]). We applied an ancestry tree search by searching all papers that refer to the review papers that were available in the Web of Science database. Second, we performed a keyword search of electronic databases (e.g., ABI/INFORM, Emerald, Elsevier, EBSCO, and ProQuest Dissertation Publishing) using "advertising creativity," "ad creativity," "advertisement creativity," and "advertising creative," "ad creative," and "advertisement creative" as key words, followed by a search with key words that relate to the two main dimensions of advertising creativity ("original*," "novel*," "newn*," "unexpected*," "divergen*," "innovati*," "incongru*," "relevan*," "appropriate*," "useful*," and "meaningful*" combined with "advertis*"). The database search was complemented by a search on Google Scholar. Third, we performed a manual search of the journal outlets that turned out to be major sources for articles on advertising creativity. Fourth, once we identified a paper, we examined the references in a search for additional studies. The search period covered all papers (published and unpublished) that were available by December 2018. The retrieval approach was consistent with recommendations in the literature ([35]) and closely followed the steps taken in recent meta-analyses published in marketing ([69]; [88]).

After identifying manuscripts for potential inclusion in the data set, we applied inclusion and exclusion criteria to determine which manuscripts to retain. We included all empirical studies that measured or manipulated advertising creativity (as described previously) and provided estimates on its effects on consumer responses. We excluded any manuscripts outside this scope. For instance, we excluded studies that investigated nonconsumer response to creative ads (e.g., advertisers; [84]), or studies on creative media choice, but not creative ads ([16]). We also excluded studies that failed to provide sufficient data for the meta-analysis and for which necessary data could not be retrieved from the authors.

To avoid duplications in the data set, a document with original analyses and findings by the same authors (e.g., journal article, working paper, conference paper) is called a "paper." In some papers, the authors analyzed more than one distinct data set (e.g., a paper with several experiments), while some data sets were analyzed in more than one paper (e.g., a study that was published as a conference paper and a journal paper). The analysis is based on data sets. Each data set can provide single or multiple effect sizes that refer to the effect of advertising creativity on any consumer response variable. The search resulted in 67 usable papers covering 93 data sets (see Web Appendix Table 1). The sample includes journal articles, book chapters, working papers, unpublished theses, and conference proceedings, thus reducing the risk of a biased representation of the state of research because of the source of publication. The variation of sources is similar, and the number of papers and data sets is higher than in other major meta-analyses in marketing ([12]; [87]).

We categorized the consumer response variables measured in the studies based on the conceptual framework (see Figure 1). Specifically, we classified consumer responses in terms of immediate responses (affect, processing, and signals) and lasting outcomes related to the ad and brand (none of the data sets provided data for sales). The outcome responses were further divided based on attitude and memory responses. In addition, we identified a few consumer responses that did not fit in either category (e.g., actual creativity, brand familiarity, willingness to pay). Because these consumer response variables appeared either in only one or two data sets or in only one paper, we eliminated them from further analysis.[ 6] We did this to ensure a minimum degree of generalizability, because a meta-analysis should provide a high degree of generalization and thus, requires more information than a single manuscript or a single-study manuscript followed by a replication study. This left 878 effect sizes. For an overview of the consumer response variables and categorization scheme, see Table 1.

Graph

Table 1. Variables Used in the Meta-Analysis.

1 ^a The moderator variable is measured at the effect-size level, while all other moderators are measured at the data-set level.

2 Notes: Intercoder reliability is provided for all high-inference coding with AR = agreement rate and α = Krippendorff's alpha.

In terms of creativity moderators, we coded the variables at the effect size level, meaning that multiple effect sizes from one data set can be assigned different codes. Specifically, we coded whether creativity was assessed as originality only, as appropriateness only, as an interaction effect between originality and appropriateness, as a multidimensional concept (including originality, appropriateness, and potentially more dimensions), or as a holistic concept (measured with a single item "creative" or corresponding multiple items or manipulated as such; this is the base alternative in the model). As an illustration, [86] presented results based on originality and appropriateness separately, as well as for the interaction between the two allowing us to code three types of measurements for each of the variables studied. Although our main interest is in comparing a bipartite view of advertising creativity with a view of advertising creativity as originality only, this coding process allows a more complete understanding of how different assessments of creativity affect consumer response.

In terms of communication context moderators, we dummy coded the variables on the data-set level. Specifically, we coded the data sets 1 if the advertised category was a high-involvement product and if the advertised brand was familiar. In addition, we added three control variables that captured substantial differences between studies and that could be related to the context variables (medium, year, and award). Two authors independently assigned variables in the primary studies to consumer responses and coded the moderators and control variables based on the information available in each study. The agreement rate was above 98% (Krippendorff's alpha =.932), and inconsistencies were resolved by discussion.

The effect size metric selected for the meta-analysis was the correlation coefficient; higher absolute values of the coefficient indicate a stronger influence of advertising creativity on consumer responses. For papers that reported other measures (e.g., Student's t, mean differences), we converted those measures following guidelines for meta-analysis ([53]; [64]).[ 7] We adjusted all correlations for unreliability. When a paper did not report the reliability, or when the paper used a single-item measure, we used the mean reliability for that construct across all studies, following the procedure in previous meta-analyses in marketing (e.g., [43]).

We dealt with integrating dependencies between effect sizes using the following approach. When a data set provided findings for different consumer response variables, we treated the findings as independent, because we integrated and analyzed the estimates for each consumer response variable separately. Some data sets reported multiple relevant tests for the same consumer response variable. We accounted for the dependencies of the effect sizes and the nested structure of the meta-analytic data by using a mixed-effects multilevel model ([67]). We estimated the following model:

${\text{r}}_{\text{ij}}={\text{γ}}_{\text{00}}+{\text{μ}}_{\text{0j}}+{\text{e}}_{\text{ij}},$

Graph

1

where i = 1, 2, 3...I effect sizes, j = 1, 2, 3...J data sets. This formula estimates the average effect size γ₀₀, the deviation of the average effect size in a data set from γ₀₀ (μ_0j), and the deviation of each effect size in the kth data set from γ₀₀ (e_ij). The latter two terms have variances that follow a normal distribution and are uncorrelated.

To address publication bias, we computed fail-safe Ns ([72]), which represents the number of additional effects with null results needed to render the results for an integrated effect size not statistically significant at p =.05. The fail-safe Ns were calculated for all statistically significant integrated effect sizes (p <.05) using the effect size estimates that were adjusted for measurement error. Furthermore, we provided a homogeneity test as an aid in deciding whether the observed effect sizes were more variable than would be expected from sampling error alone. If they are, there is a strong basis for including moderators. The homogeneity test involves the Q statistic, in which the distribution is similar to a chi-square with K − 1 degrees of freedom ([32]).

If the homogeneity test indicated heterogeneity, we proceeded with a moderator analysis. We added the moderators specified by the hypotheses and the control variables simultaneously to Equation 1 and ran multilevel meta-regression models in hierarchical linear modeling separately for the major outcome variables. The model was a mixed-effects model, because fixed effects for the moderators were considered in addition to random components. We specified the following model:

$$\begin{gathered} {\text{r}}_{\text{ij}}={\text{γ}}_{\text{00}}{\text{+ γ}}_{\text{01}}\times \left({\text{involvement}}_{\text{j}}\right)+{\text{γ}}_{\text{02}}\times \left({\text{familiarity}}_{\text{j}}\right) \\ +{\text{γ}}_{\text{04}}\times \left({\text{medium}}_{\text{j}}\right)+{\text{γ}}_{\text{05}}\times \left({\text{year}}_{\text{j}}\right)+{\text{γ}}_{\text{06}} \\ \times \left({\text{award}}_{\text{j}}\right)+{\text{γ}}_{\text{10}}\times \left({\text{originality}}_{\text{ij}}\right)+{\text{γ}}_{\text{20}} \\ \times \left({\text{appropriateness}}_{\text{ij}}\right)+{\text{γ}}_{\text{30}}\times \left({\text{interaction}}_{\text{ij}}\right) \\ +{\text{γ}}_{\text{40}}\times \left({\text{multidimensionality}}_{\text{ij}}\right)+{\text{γ}}_{\text{50}} \\ \times \left({\text{partial correlation}}_{\text{ij}}\right)+{\text{u}}_{\text{0j}}+{\text{e}}_{\text{ij}}, \end{gathered}$$

Graph

2

where r_ij is the ith effect size describing the relationship between advertising creativity and the respective consequence variable reported within the jth data set.

Assuring the robustness of the model required a sufficient sample size. The major restriction is often the higher-level sample size, and the literature recommends a sample of around 50 to avoid biased estimates of the second-level standard errors ([54]). Thus, we applied the model only to the outcome variables in the data set with a sufficiently large sample of data sets: A_ad and A_brand (43 and 44 data sets, respectively).

To investigate the different processes that explain how advertising creativity works, we developed a correlation matrix including integrated effect sizes of the consumer responses to advertising creativity and added integrated effect sizes for the interrelationships between the consumer response variables. We followed recommendations in the literature about collecting meta-analytic data for the correlation matrix, deciding about sample size, analytical decisions, and reporting ([ 6]). We searched the papers in the meta-analysis for correlations for the interrelationships between consumer response variables. For a construct to be included in such analysis, multiple study effects must relate it to every other construct in the model. Therefore, no additional variables shown in Table 1 could be considered. For example, because we did not find correlations between sender effort and recall or memory measures, the latter could not be included in the model. We found at least three correlations for each relationship, which equals or exceeds the requirements of other meta-analytic correlation matrices found in the literature ([28]). We integrated and adjusted the correlations in the same way as the correlations between advertising creativity and consumer response variables. That is, we first adjusted all correlations for unreliability. We accounted for the dependencies of effect sizes and the nested structure of meta-analytic data by using a mixed-effects multilevel model as described previously ([67]).

We then used this correlation matrix (see Web Appendix Table 2) as input in a structural equation modeling (SEM) analysis using the maximum likelihood method. The matrix was based on 449 correlations, and the harmonic mean of the cumulative sample size for each cell equaled 1,293. Each construct was measured with a single indicator in the structural model. We fixed the error variances for these indicators to zero because we had already considered measurement errors when we integrated the effect sizes. We used the harmonic mean of the cumulative sample size underlying each integrated effect size (i.e., effect size cells comprising each entry in the correlation matrix) as the sample size for the analysis.

Table 2 reports the integration of the reliability-corrected correlations between advertising creativity and all consumer response variables.

Graph

Table 2. Influence of Advertising Creativity on Consumer Responses (H₁).

• 3 ^†p <.10.
• 4 *p <.05.
• 5 **p <.01.
• 6 ***p <.001.
• 7 ^a These variables are used to test H₂–H₇.
• 8 Notes: Only relationships for which effects were available in more than one paper and/or more than two independent data sets are shown. The corrected average correlation coefficients (r) are the sample size-weighted, reliability-corrected estimates of the population correlation coefficients. The fail-safe N indicates the number of nonsignificant, unpublished (or missing) effects that would need to be added to a meta-analysis to reduce an overall statistically significant (p <.05) observed result to nonsignificance.

Looking at immediate responses, we found statistically significant effects on affect in terms of positive affect and perceived humor. Interestingly, although positive affect has been studied more, the effects of humor were significantly stronger as indicated by nonoverlapping confidence intervals (95% CI for positive affect [.198,.388] vs. humor [.428,.832]). We also found significant positive effects on processing in terms of attention, interest in the ad, and ad processing, but only a marginal effect on complexity and positive thoughts. The effects on attention, interest in the ad, and ad processing were comparable in size (95% CI for attention [.218,.592], interest in ad [.215,.615], and ad processing [.015,.659]). Furthermore, advertising creativity had statistically significant positive effects on perceived signals: sender effort, brand value/quality, brand trust, and brand credibility. These effects were comparable in terms of size (95% CI for perceived sender effort [.282,.510], value/quality [.171,.407], brand trust [.171,.603], and brand credibility [166,.628]).

Turning to outcome responses, advertising creativity had a statistically significant effect on all ad responses: A_ad, ad recall, and ad recognition. The strongest and most widely studied effect was that on A_ad. In terms of brand responses, the effects followed a similar pattern: A_brand was the most widely studied variable and statistically significantly affected. We also found a statistically significant positive effect on purchase/behavioral intention and brand memory, but not on brand recall or brand recognition. Overall, the pattern of results support H₁ by highlighting that advertising creativity has positive effects on consumer reactions in terms of ad and brand. Answering RQ₁, we found that the effect on A_ad was statistically significantly larger than that on A_brand and purchase intentions (95% CI for A_ad [.407,.575] vs. A_brand [.235,.399]) and purchase intention [.225,.387]), and that the effects on ad recall ([.214,.408]) were significantly larger than the effects on brand memory ([.072,.208]). This suggests that ad responses are more affected than brand responses. Related to RQ₂, the pattern of results suggests that effects of advertising creativity are stronger for attitudes than for memory. A_ad was statistically significantly different from ad recognition (95% CI for A_ad [.407,.575] vs. ad recognition [.107,.307]), and marginally different from ad recall ([.214,.408]). Similarly, the effect on A_brand was significantly stronger than the effect on brand memory (95% CI for A_brand [.235,.399] vs. brand memory [.072,.208]).

All homogeneity tests (except for brand memory) were statistically significant at p <.05 and showed that the variation in effect sizes cannot be explained by sampling error alone. The fail-safe N indicates that the statistically significant integrated correlations do not suffer from publication bias according to [72] rule of thumb (fail-safe N should be at least 5 times the number of effects plus 10).

Table 3 presents the results for the multilevel moderator regression model for the relationship between advertising creativity and A_ad and A_brand. To investigate whether the positive effects of advertising creativity depend on the type of assessment used, we examined the moderating effect of creativity assessments. The analysis showed that relying only on originality led to lower effect sizes for A_ad and A_brand; thus, H₂ was supported. We found a similar pattern for assessments relying on appropriateness only and for interaction effects, although the negative effect was only marginally significant for the latter when it came to A_brand. The findings also showed that multidimensional measures of advertising creativity led to stronger effect sizes for A_brand, but not for A_ad. Overall, this pattern of results suggests that assessing advertising creativity only in terms of ( 1) originality, ( 2) appropriateness, or ( 3) an interaction effect between the two will lead to an underestimation of the effects. From a managerial perspective, the result also suggests that a multidimensional view of advertising creativity is the most relevant, as brand responses are more important than ad responses.

Graph

Table 3. Influence of Moderator Variables on Effect Sizes: Multivariate Meta-Regression Analysis Results (H₂–H₄).

• 9 ^†p <.10.
• 10 *p <.05.
• 11 **p <.01.
• 12 ***p <.001.

We then turned to the moderating effect of the communication context. The results showed stronger effects on A_ad and A_brand for high-involvement products; thus, H₃ was supported. Furthermore, the effects on A_ad were marginally stronger for unfamiliar products, but there was no statistically significant difference in terms of A_brand. Thus, H₄ was only partially supported. The control variables showed that using a partial correlation coefficient led to smaller effects on A_ad. None of the remaining control variables affected A_ad. However, the effects on A_brand were higher for audiovisual media (TV/movies) and marginally lower for award-winning ads. We did not find any statistically significant differences in terms of year of study.[ 8]

To better understand why advertising creativity has positive effects on consumer responses, we performed a SEM analysis of the different models using the meta-analytic correlation matrix (cf. Web Appendix Table 2). Table 4 presents the results of the SEMs (standardized coefficients and model fit statistics). As we suggested alternative models implying that the relationship between advertising creativity and A_ad is mediated by more than one mediating variable, we added a path between advertising creativity and A_ad that captured alternative processes to each model. All three individual models showed a very good model fit, and all paths were statistically significant and in line with the suggested effects; thus, H₅, H₆, and H₇ were supported.

Graph

Table 4. Coefficients and Fit Indices of the Meta-Analytic SEMs (H₅–H₈).

• 13 ^†p <.10.
• 14 *p <.05.
• 15 **p <.01.
• 16 ***p <.001.

The model that combines the three individual models showed a comparatively weak fit but was significantly improved by adding the proposed relationships between processing and perceived sender effort and positive affect and perceived sender effort suggested by the full model (Δχ²/d.f. = 96.527/5, p <.001; see Figure 2). To determine whether the full model provided a better explanation than the three parsimonious models that were nested within it, we compared the fit of the full model that was restricted to any of the nested models with the fit of the full model with unrestricted paths. The model fit worsened significantly when it was restricted to the affect transfer model (Δχ²/d.f. = 1,629.935/8, p <.001), the processing model (Δχ²/d.f. = 1,733.093/8, p <.001), or the signaling model (Δχ²/d.f. = 1,528.916/8, p <.001). Thus, the full model provides an explanation that goes beyond the explanatory power of each nested model; H₈ was empirically supported. Interestingly, in the full model, the mediating effect of A_ad on A_brand dropped from around.5 in the individual models to a marginally significant effect of.078 (Δχ²/d.f. = 96.512/1, p <.001). This suggests that the effect of advertising creativity on brand response is only weakly mediated by ad response, which adds additional insight into RQ₁ about the effects of creativity on ad versus brand response.

We performed two additional analyses to further explore how well the three models explain the effects of creativity on consumer response. First, we compared how much of the variance in A_ad was explained directly by advertising creativity and indirectly by either process suggested by the three individual models (we could not apply this comparison to A_brand, as there was no direct effect of creativity on A_brand in the model). We computed the proportion of mediation as the ratio of indirect to total effect; that is, the indirect path(s) was/were divided by the sum of the direct path and indirect path(s) ([36]). The proportion of mediation via positive affect was 26.8%, via ad processing was 28.3%, and via perceived sender effort was 33.9%. When we tested the mediation paths in the full model against each other by restricting two corresponding paths at a time (see Web Appendix Table 3), we found no differences between the paths from advertising creativity to any of the three mediators (positive affect, ad processing, and sender effort). However, the effect of sender effort on A_ad was significantly different and stronger than the effect of either positive affect or ad processing on A_ad. The findings indicate that signaling explains more variance in A_ad than the two other models, thus providing the strongest explanation for the effect of creativity on A_ad of the three models.

Second, we compared the theoretical explanation offered by the full model between the two dimensions of creativity by using correlation matrices that considered the variable relationships with either originality or appropriateness instead of creativity (see Web Appendix Table 4). The results showed that the positive effects on ad processing are equally strong for both dimensions. However, affect transfer mainly explains the effects of originality as indicated by the fact that the path from creativity to positive affect was statistically significant for originality, but not for appropriateness. When it comes to signaling, however, appropriateness seems more important, as indicated by the significantly stronger link between creativity and sender effort.

In this article, we offer a comprehensive synthesis of the effects of advertising creativity on consumer responses. The study highlights the importance of advertising creativity by showing robust positive effects on a wide range of immediate and outcome responses. The effects are stronger for ad responses compared with brand responses and for attitudinal compared with memory outcomes. Moderation analyses show that the effects of advertising creativity are weaker when creativity is assessed as originality only, compared with a bipartite comprising originality and appropriateness. This suggests that the effects of advertising creativity go beyond those of originality alone. The results further show that advertising creativity has stronger effects in high-involvement contexts, and that effects on ad response are (marginally) stronger for unfamiliar brands. Furthermore, we find empirical support for all three theoretical accounts (affect transfer, processing, and signaling) used in the literature, but also that a full model (where the three accounts are considered jointly) best explains the effects of advertising creativity on consumer outcome response. In the full model, the effect of the three advertising creativity mediators (positive affect, ad processing, and perceived sender effort) on brand response is only marginally mediated by ad response, suggesting that although ad responses are generally more affected than brand responses, they are not needed for advertising creativity affect brand response. Additional analyses show that affect transfer mainly explains the effects caused by originality and that signaling provides the strongest account of advertising creativity in terms of ad response.

Although marketing researchers and practitioners tend to agree that advertising creativity is important, there are contrasting views on what advertising creativity is, and how and when it can lead to positive outcomes. Through this meta-analysis, we provide a synthesis of the growing, but dispersed, literature on advertising creativity, thus building a common foundation for future studies of this important topic. The results inform about important outcome variables and moderators of advertising creativity effects. The meta-analytic findings can serve as benchmarks for future advertising creativity studies, as well as for studies dealing with other ad execution elements. Future findings can be compared against the meta-analytic results in terms of explained variance as a measure of advertising effectiveness. The results also have several implications for future studies of advertising creativity.

First, we offer an empirically validated understanding of how advertising creativity works. The pattern of results suggests that advertising creativity has a role to play in stimulating positive consumer responses that goes beyond being a source of attention. If the attention-grabbing nature of advertising creativity were the key benefit, its effects should be greater for memory rather than attitudinal responses, and in communication contexts where consumers are less likely to attend to and process ads, such as for low-involvement products and for unfamiliar brands ([19]; [65]), which is not in line with the empirical results. Although claims that advertising creativity enables advertising to "cut through clutter" and make advertising more memorable ([65]) are true, they risk directing focus away from attitudinal consumer responses, which are more affected. The fact that advertising creativity has stronger effects in high-involvement contexts suggests that processing is important for the effects to occur. It also raises the question of what to expect from advertising creativity in contexts where consumers are unlikely to pay attention to and process ads, such as digital and mobile media. The meta-analysis did not include any such studies, but the results suggest that effects should be weaker in media such as smartphones where focus is very directed at other focal tasks ([58]). At the same time, effects should be stronger for advertising content in own channels and in media where consumers voluntarily seek out advertising ([70]). However, future research is needed to explicitly study the role of advertising creativity in these contexts.

Second, we contribute insights into how to define and assess advertising creativity. In line with the creativity literature ([ 2]; [73]), the results indicate that creativity is not just about originality. A bipartite definition and multidimensional assessments of creativity offer better explanations of the effects (for a similar argument, see [ 3] and [60]). This suggests that researchers should be mindful when using the term advertising creativity and restrict it to studies of original and appropriate ads. When studying original advertising only, the term creativity should be avoided. It also suggests that the reliance on advertising awards as an operationalization of advertising creativity is not valid, as such awards tend to focus on originality ([15]; [41]). The fact that empirical studies have found positive effects of original and award-winning ads, however, is reassuring, as the results suggest that, if anything, those studies underestimate the effects.

Third, we contribute to the theoretical understanding of how advertising creativity works. The findings show that the different theoretical accounts of advertising creativity available in the literature are complementary, but also that they have different relationships with creativity dimensions. Our meta-analytic path analysis show that originality primarily stimulates affect transfer, whereas appropriateness is more important for signaling. We also find that signaling has the highest explanatory value. Again, this reinforces the notion that a bipartite view of advertising creativity is most relevant, as ads that combine originality with appropriateness allow these mechanisms to work simultaneously, whereas original ads do not. It also suggests that future studies of advertising creativity should include more comprehensive theoretical frameworks than what has previously been the case. Together, these insights offer the basic building blocks for a more complete processing model of advertising creativity called for by [85].

Fourth, the finding that the three theoretical mediators of advertising creativity have direct effects on brand response (A_brand) that are only weakly mediated through ad response (A_ad) adds further to our understanding of how advertising creativity works.[ 9] It shows that although creativity has stronger effects on ad responses than brand responses, these effects are not necessarily dependent on ad response. Again, this pattern can be understood in terms of the combination of (high) originality and (high) appropriateness in creative ads. In line with [78] finding that originality has advantages in terms of attention and that appropriateness stimulates downstream effects and brand response, advertising creativity allows the two to work in parallel, which also has more direct brand outcomes. This finding is in line with the signaling account of advertising creativity that suggests a more direct effect on the brand. For researchers, it suggests that when studying the effects of advertising creativity, brand (and sales) responses must be included through direct measures rather than relying on A_ad or other ad responses as proxies of such effects.

Overall, the empirical results provide convincing evidence of the positive effects of advertising creativity on consumer responses and thus highlight the need for marketing scholars to consider not only media investments (ad spend; [38]; [80]) but also creativity investments in models of how advertising work.

For marketers, we contribute a systematic account and empirical evidence of the value of advertising creativity. Specifically, we offer important insights into how, when, and why to invest in advertising creativity. Given the ongoing debate about the value of creativity in advertising ([27]; [66]), this contribution is timely and useful. It also shows no evidence of advertising creativity becoming less (or more) effective over time.

When it comes to how to invest, [68] found that many marketers make suboptimal decisions regarding investments in advertising creativity. We suggest that a tendency to focus on originality might be the root of this problem. Creativity is more than originality, and by incorporating appropriateness consumer response will be more positive. To achieve this, marketers must find ways to assess advertising creativity. This is easier said than done, given that creativity judgments are subjective and vary across context and time. We find that award-winning ads lead to marginally weaker brand response, suggesting that consumer rather than professional judgments should be used. This supports [ 3] argument that marketers should involve consumers more in advertising development. Whereas there is a growing literature focusing on consumers as cocreators of advertising ([17]; [81]), consumers could also be engaged as prejudges of advertising. A post hoc analysis of the role of ad judges provided additional support for this notion. Specifically, we coded a variable that distinguished between ads that were judged to be creative by either consumers, by experts, or selected from award shows. As some studies did not provide details on ad judges, we first ran analysis of variance models for a combination of all three outcome responses (A_ad, A_brand, and intentions) to ensure sufficiently large sample sizes. We found significant effects (F( 2, 351) = 4.931, p =.008) on outcome response. The effects were stronger when consumers judged advertising creativity (.373) compared with experts (.300) or award shows (.193). When we analyzed the three responses separately, the effect held for A_brand and intentions, but not for A_ad. As brand outcomes are more valuable for marketers, this reinforces the potential in allowing consumers to (pre)judge advertising creativity.

When it comes to when to invest, the results suggest that advertising creativity has positive effects in general but also that the effects are stronger for attitudinal rather than memory response and marginally stronger in audiovisual media (TV/movies vs. print/outdoor). Furthermore, the effects are stronger for high-involvement contexts. For marketers, this challenges the established view of advertising as a tool for gaining attention and suggests that creativity is especially valuable in contexts where consumers are likely to process advertising. Although we studied product involvement, this logic should also hold for media context involvement, meaning that creativity is more likely to work in situations where more focused ad processing occurs. Thus, advertising creativity should be more important for media contexts in which consumers voluntarily direct their attention to, or are forced to focus directly on, advertising than in in media contexts that rely on incidental and divided attention (see also [17]; [70]).

We also find that advertising creativity has marginally stronger effects for unfamiliar compared with familiar brands. However, this effect is related only to ad rather than brand response. As suggested by [11], ad response is a strong indicator of brand response for unfamiliar brands (as consumers have little other information on which to base evaluations), suggesting that this finding is still managerially important. By investing in advertising creativity, such brands can increase the value of their advertising to consumers ("advertising equity"; [70]). Taken together, this suggests that advertising creativity is especially valuable when establishing a new brand in the market.

When it comes to why advertising creativity works, the mechanisms underlying its positive effects are more profound than many marketers might think. An in-depth understanding of how affect transfer, processing, and signaling jointly contribute to brand response help make investments in advertising creativity less risky ([85]). Although marketers who focus on originality can expect positive effects due to affect transfer, they will miss out on the potential effects of signaling and appropriateness. By investing in bipartite advertising creativity, marketers can increase the chances of their ads being liked, processed, and interpreted as signals of what the brand has to offer. It also means that there is little risk that positive effects will be for ad response only.

From a managerial perspective, the effects of signaling are especially important to consider, as they offer the strongest explanation for the effects on ad response and because appropriateness is especially important in high-involvement and low-familiarity contexts, where advertising creativity also has the strongest effects. It suggests that advertisements can produce effects by way of the signals they send rather than the specific messages they convey. Signals are especially important in situations where there is information asymmetry between marketers and their customers ([13]; [46]). This is arguably the case for unfamiliar brands and high-involvement products as well as in other situations where the decision-making process is complex, such as in business-to-business, business-to-government, and recruitment contexts ([13]; [18]). In fact, recent research suggests that the effect of advertising signals extends beyond consumers to other stakeholders, such as employees and investors ([18]), though this is beyond the scope of the present study.

Given the nature of a meta-analysis, we could study only consumer responses that previous researchers had investigated. This means, for example, that we could not consider potential negative effects of creativity on, for example, confusion, negative affect, and fear appeals. However, we found a marginally significant negative effect of complexity, suggesting that the potential downsides of creativity warrant further investigation.

Similarly, the literature review revealed a lack of studies on the effects of advertising creativity on sales (for an exception, see [68]) and the effects of advertising creativity in digital contexts, such as the effects of advertising creativity on social media influencer engagement ([34]). Future studies are needed to explore how advertising creativity works in those contexts. Studies linking the effects of advertising creativity to behavioral measures, such as brand choice or sales, seem especially important. This could be done by combining quantitative (advertising spend) and qualitative (advertising creativity) assessments of advertising investments with behavioral outcomes, for example, adding advertising creativity in marketing-mix models or adding sales as a dependent variable in experimental studies. In such efforts, additional moderators, such as clutter ([65]) and repetition ([14]), should also be considered.

As another limitation, the present study focused on consumer responses to advertising creativity only. There are several related issues in the literature that could contribute to our understanding of advertising creativity. For example, there is a vast literature on creative processes in agencies that foster creativity in advertising ([29]; [40]), and synthesizing this literature should bring additional insights to marketers. Relatedly, there should be room to further integrate the literature on advertising creativity with creativity research focusing on other marketing contexts ([ 8]; [21]) to allow for a more complete understanding of how creativity works in marketing more broadly. It is our hope that this article can contribute to this development.

Supplemental Material, 20200503_JM.18.0506.R3_web_appendix - A Meta-Analysis of When and How Advertising Creativity Works

Supplemental Material, 20200503_JM.18.0506.R3_web_appendix for A Meta-Analysis of When and How Advertising Creativity Works by Sara Rosengren, Martin Eisend, Scott Koslow and Micael Dahlen in Journal of Marketing

1 Associate Editor Wayne Hoyer

2 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

3 Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

4 Online supplement: https://doi.org/10.1177/0022242920929288

5 1 The models we use in this article focus on key variables that are discussed in the extant literature of the three theoretical accounts. We could not include additional variables presented in Table 1 because of data constraints, which we explain in detail in the "Methods" section. It should also be noted that all three accounts have primarily been developed using experimental approaches.

6 2 We excluded the following variables from further analysis (mean correlations in parentheses): negative thoughts (−.105, p <.01), other thoughts (.047, n.s.), negative feelings (.083, n.s.), actual creativity (.189, p <.01), brand familiarity (.192, p <.10), presumed influence (.309, p <.01), and willingness to pay (.429, p <.10).

7 3 Of 878 effect sizes, we converted 21 from coefficients in multivariate regressions via the formula suggested by [64]. These parameters were partial correlations, and therefore, we checked whether they had an influence on the meta-regression results by including a dummy variable that distinguishes between partial correlations and correlations. Because partial correlations did not appear in the set of correlations referring to A_brand, the dummy was included in the A_ad model only.

8 4 To further explore the role of originality and appropriateness in explaining these effects, we also tested several plausible interactions between creativity measurements and moderators (similar to the procedure in [75]). For A_ad, we tested interactions between measurements (using dummy variables for originality, appropriateness, and multidimensions, the cell sizes for interactions with "interaction only" were too small to provide a robust analysis) and the hypothesized moderating variables (involvement, familiarity). The analysis showed a significant interaction effect for appropriateness × familiarity (b = −.239, SE =.062, t = 3.830, p <.001), suggesting that appropriateness is more important for unfamiliar brands. There was also a significant interaction effect for appropriateness × involvement (b =.271, SE =.058, t = 4.643, p <.001), suggesting that appropriateness is more important in high-involvement contexts. We conducted the same analysis for A_brand, where we were able to test interactions between measurements focusing on originality and appropriateness (the cell sizes for "interaction only" and "multidimensional" were too small to provide a robust analysis). However, we did not find any statistically significant interactions.

9 5 This suppression effect is especially interesting because the A_ad and A_brand relationship is typically very strong, as evidenced in previous meta-analyses (e.g., [7]]: r =.600 [# effect sizes = 33], [22]]: r =.581 [4], [24]]: r =.624 [11]).

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By Sara Rosengren; Martin Eisend; Scott Koslow and Micael Dahlen

Reported by Author; Author; Author; Author

A Study of Bidding Behavior in Voluntary-Pay Philanthropic Auctions 

The authors investigate compliance behavior and revenue implications in winner-pay and voluntary-pay auctions in charity and noncharity settings. In the voluntary-pay format, the seller asks all bidders to pay their own high bid. The authors explore motives and boundary conditions for compliance behavior based on internal and external triggers of social norms. The voluntary-pay format generates higher revenue than the winner-pay format for charity auctions, despite imperfect compliance, but it generates lower revenues in noncharity settings. To characterize bidding strategy, the authors study time to bid, auction choice, and jump bidding and find evidence that bidders in voluntary-pay auctions more commonly use jump bidding and late entry. The findings have important implications for marketing managers, augmenting the growing stream of empirical auction studies and work on corporate social responsibility. Specifically, combining an auction with a charitable cause may result in increased revenues, but managers should ensure that they are accounting for differential compliance rates between auction formats. Even if low-compliance bidders can be identified and screened out, doing so is not advantageous, because noncompliant bidders bid up prices.

Online Supplement: http://dx.doi.org/10.1509/jm.16.0476

In a voluntary-pay auction format, all bidders—both winning and losing—are asked to pay their highest bid. This format contrasts with a winner-pay format, in which only the winners are asked to pay. Little is known about voluntary-pay auctions’ impact on auction revenue and how bidders bid in voluntary-pay auctions, their willingness to pay, and bidding strategies. This study addresses this lack by examining voluntary-pay and winner-pay auctions in charity and noncharity settings. We examine whether the voluntary-pay format increases collected revenues and, if so, whether this is merely a charity-related phenomenon or extends to noncharity settings.

In winner-pay auctions, which include most commonly used nonphilanthropic auction formats, one bidder wins the item and pays for it. Although payment compliance is known to be imperfect even in legally unambiguous settings, payment enforcement is not generally an issue if the seller can ensure that payment is delivered prior to the buyer obtaining the item.

Payment compliance is a far greater problem in voluntary-pay auctions, where all bidders are asked to pay whether or not they win. Although a substantial theoretical literature has advocated the use of all-pay auctions, this body of theory largely assumes perfect payment compliance. Therefore, a pressing question is whether there is high payment compliance in legally unenforceable settings in which the participating bidder commits to pay regardless of winning, specifically in a voluntary-pay auction context. We examine the factors that influence this rate of compliance.

To study the conditions that foster payment compliance, we test different appeals for compliance. Specifically, we explore motives and boundary conditions for compliance behavior based on internal and external triggers of positive (pride) versus negative (shame) social norms.

In addition, we conduct two field studies to study voluntary-pay auctions and payment compliance in a real-world setting to determine whether this format offers any benefits. We study these issues on an active large-scale auction website with a strong reputation for charity auctions (though thiswebsite also runs noncharity auctions). It operates primarily in one major North American city and has raised $4 million for local charities in the past decade. The auctions run on the site are ascending-bid (English-language) auctions, and sellers have flexibility in terms of format.

In analyzing strategic considerations, we focus on three factors:

1. The strategic interaction between jump bidding and late bidding in a field setting. This interaction is far more important in settings with contractual obligations for losing bidders, as waiting to bid might be a better option.
2. The opportunity cost of entering a bid in an auction. This cost is far more important when entry involves a nonbinding contractual obligation—the opportunity cost being an alternative for obtaining the same item.
3. Contract compliance when social consequences may differ between settings: a noncharity setting, in which the social contract is with only the contracting party, and a charity setting, in which the social contract is broader.

To study these factors, we estimate a model with four components: time to bid, auction choice, jump bid, and propensity to pay.

The auctions we study are not strictly all-pay auctions owing to imperfect collection; therefore, we call these “voluntary-pay” auctions. They are motivated similarly to all-pay auctions: all bidders, including losing bidders, are asked to pay an amount equal to their highest bid. In all-pay auctions, payment compliance is assumed to be fully enforceable (which may be inconsistent with real constraints; Budig, Butler, and Murphy 1993; Posner 1977). Voluntary-pay auctions relax that full-enforceability assumption. In contrast to both these formats, in winner-pay auctions only the bidder with the highest bid is asked to pay. We summarize the differences between the winner-pay, all-pay, and voluntary-pay auction formats in Table 1.

All-pay noncharity auctions belong in the class of auctions that satisfy revenue equivalence under standard conditions, even though, in general, revenue equivalence has not been established empirically (e.g., Gneezy and Smorodinsky 2006). However, this equivalence does not hold for ascending-bid all-pay auctions, which we use, because in dynamic all-pay auctions a bid reveals something about the value of the bidder. Another difference is that in the sealed-bid auction, bidders have no opportunity to place a second bid after they find that they are not the high bidder (and that they need to pay their bid anyway) and thus need to consider this possibility before placing a bid. In the ascending-bid auction, bidders can update their bids and adjust their strategy as the auction progresses. They are more likely to continue bidding due to the commitment already made, which may result in bidding frenzy (Heyman, Orhun, and Ariely 2004).

Furthermore, in charity auctions, revenue equivalence between formats is not theoretically expected. When participation rates are exogenous and pledges are binding (neither is the case in the current study), all-pay charity auctions are expected to dominate other common formats in terms of revenues (Engers and McManus 2007; Goeree et al. 2005; Schram and Onderstal 2009).

Revenue in the voluntary-pay auction consists of the ending price and the compliance rate. Because compliance is not enforceable, revenue depends on losing bidders’ propensity to pay. However, bidders with a low propensity to pay are likely to bid more aggressively. Furthermore, we expect greater bidder entry into the voluntary-pay than into the all-pay auction, which also has a positive effect on ending price. Higher revenue is likely to result as long as a considerable proportion of losing bidders pay. However, this possibility may not hold for non-charity auctions, where bidders tend to be less likely to enter and may bid less aggressively.

Issues with payment compliance are commonplace in consumer auctions. For example, eBay has a help page for sellers who do not receive payments.1 In philanthropic settings, the compliance problem is exacerbated. Even with the most explicit legal contracts, perfect enforcement of social responsibility is nearly impossible from a legal standpoint (e.g., Budig, Butler, and Murphy 1993; Posner 1977).2 However, in recent years theoretical and experimental articles have advocated the use of the all-pay auction, particularly for charity. These articles, which we discuss next, offer assurance that all-pay auctions are sound under the assumption that enforcement is perfect.

All-pay field studies and endogenous entry. A comparison of revenues for sealed-bid auction formats—including all-pay charity auctions, first-price charity auctions, and second-price charity auctions—revealed that all-pay auctions were revenue-dominated by winner-pay auctions, a result the authors attributed to low participation and low bids (Carpenter, Holmes, and Matthews 2008). This highlights the importance of accounting for endogenous entry, which we do. However, that finding emerged in a sealed-bid context, in which jump bidding and timing do not play a role, and with binding payment with perfect compliance—possible in the smaller controlled setting.

Another study also used a sealed-bid format to compare voluntary contribution mechanisms, lottery, and all-pay auctions in a door-to-door fundraising field experiment (Onderstal, Schram, and Soetevent 2013). Results indicated that the all-pay condition performed worse than all other formats owing to lower participation—reinforcing our focus on endogenous participation.

A field experiment on Taskcn, a large Chinese crowdsourcing site, modeled the submissions of solutions as a first-price sealed-bid all-pay auction, because all submitters had to expend some effort regardless of whether they won (Liu et al. 2014). Results showed that providing higher rewards led to more and higher-quality submissions but at the cost of entry deterrence.

Studies 1 and 2 involve clean experimental manipulations, with each setting in isolation. Thus, summary measures can be compared from settings, and differences in these measures can be attributed to the differences between the settings.

In contrast, in auction field comparisons—the kind collected from real auction sites such as eBay and the platform used in the current research—a conventional approach is to compare revenues for different overlapping or simultaneous auctions for the same items side by side. This practice is common in empirical auction research (for examples, see Table 2). Although this approach would be unacceptable in laboratory methodology, it is not only acceptable but also desirable in field methodology. In field auctions, the notion of ceteris paribus cannot be routinely assumed between auctions run at different times. Therefore, it is important to run alternative formats during approximately the same time frame. Second, one cannot typically exclude real-world bidders from bidding in other formats or designs that overlap. Third, giving bidders a choice between auctions is advantageous because it indicates preference. Study 3’s approach clearly has shortcomings, and we urge readers to be cautious in comparing formats in this study, because comparisons are based on mixed experimental manipulations.

TABLE: TABLE 1 Core Principles of Different Auction Types

The hypotheses are summarized in the conceptual framework in Figure 1. We first identify the conditions that foster payment compliance. Without payment compliance, voluntary-pay auctions will not generate any revenues from losing bidders.

Social pressure is effective in generating compliance with costly social norms. Field experiments (held during elections) designed to explore whether positive (pride) or negative (shame) social pressure is effective in reinforcing voting (the social norm) revealed that negative pressure was a stronger motivator (Panagopoulos 2010). More closely related to our current work is research on tax compliance that showed social pressure to be critical in increasing payment compliance in tax scenarios (Bobek and Hatfield 2003). However, results revealed that moral obligation—the intrinsic pressure—is key in that respect, thus creating what we call internal social pressure, which primes intrinsic motives. We distinguish between internal and external social pressure, as well as between negative and positive framing. The manipulations examine appeals to internal moral code versus external social pressure for compliance (Trevino 1986). We contrast positive versus negative social appeals for compliance, wherein positive and negative appeals can be through either external consequences (Bearden and Rose 1990; Burke and Logsdon 1996) or internal consequences (Yi 1990). We state these relationships in a single hypothesis.

H1a: Social pressure increases payment compliance by bidders in voluntary-pay auctions.

Results of a charity field experiment with various formats conducted in preschools revealed that participation in the all-pay format is lower than in the other formats (Carpenter,

Holmes, and Matthews 2008), also observed by Schram and Onderstal (2009). Thus, as we state in H2b, owing to costly entry in voluntary-pay auctions, bidder participation is expected to be lower. In addition, the presence of social pressure, which (per H1a) increases pressure to pay, is expected to further decrease entry, as potential bidders who anticipate the increased compliance pressure will not enter the auction. Accordingly, we conjecture that social pressure reduces participation.

H1b: Social pressure decreases participation in voluntary-pay auctions.

Theoretically, the expectation is that although individual bidders reduce their willingness to pay in all-pay auctions, total revenues are higher in all-pay auctions than in any other form of winner-pay auction when all potential bidders participate (Engers and McManus 2007; Goeree et al. 2005). However, with endogenous participation (Liu et al. 2014), entry in all-pay auctions is generally highly sensitive to incentives. Accordingly, when bidders enter because of social pressure (per H1a), a negative incentive, we expect that (applying similar rationale as for H1b) they are willing to pay less (Carpenter, Holmes, and Matthews 2008), resulting in the following hypothesis:

H1c: Social pressure reduces bidders’ willingness to pay in voluntary-pay auctions.

Choice of auction format. The remaining hypotheses pertain to the theory for the charity setting with choice between formats. The choice is between a voluntary-pay and a winner-pay format for the same item. We focus on bidders’ propensity to pay (if they lose) in the voluntary-pay auction. Conceivably, some degree of adverse selection may occur whereby bidders with lower propensity to pay (upon losing) find engaging in the voluntary-pay auction to be less costly in expectation. Adverse selection in auction choice has been previously documented (Dewan and Hsu 2004), and we extend this to format choice in auctions, resulting in the following hypothesis:

TABLE: TABLE 2 The Literature on Field and Natural Auction Experiments

H2a: Bidders with lower propensity to pay are more likely to bid in voluntary-pay auctions than in winner-pay auctions.

Endogenous entry. Bidder entry is a particularly problematic dimension in a voluntary-pay context, because it can result in complete reversal of revenue predictions (e.g., Carpenter, Holmes, and Matthews 2008). However, in contrast to prior work using sealed-bid auctions (Carpenter, Holmes, and Matthews 2008), we consider an ascending-bid format, in which entry tends to be less costly (because entry cost is generally lower than the amount pledged). As a result, more bidders should enter an ascending-bid auction than into a sealed-bid auction, but overall we expect that the number of bidders will be lower in the voluntary-pay auction than in the winner-pay auction owing to the financial commitment.

H2b: Fewer bidders enter voluntary-pay auctions than enter winner-pay auctions.

Jump bidding. Do bidders use jump bidding and time to bid strategically to reduce competition (e.g., Isaac, Salmon, and Zillante 2007)? Jump bidding can be effective in deterring competitors from entry by signaling aggressiveness when bidding is costly (e.g., Avery 1998; Easley and Tenorio 2004). If bidding is costly, this aspect makes the auction an all-pay variant. Thus, jump bidding is a critical aspect of the present investigation into voluntary-pay auctions, because it can have animportant deterrence potential owing to the costly commitment by all bidders. Jump bids do not have that deterrence potential in winner-pay auctions because losing bids do not entail a financial commitment. Thus, we expect that if jump bids are an effective deterrent, then in voluntary-pay auctions large jump bids (especially early in the bidding process) will deter other competitors from entering or continuing to bid, thereby increasing the likelihood of winning an auction.

H3a: Jump bidding occurs more frequently in voluntary-pay auctions than in winner-pay auctions.

Late bidding. Late bidding can be effective in avoiding early commitment and bidding wars (Roth and Ockenfels 2002). Given the entry (commitment) costs in voluntary-pay auctions, bidders will have a greater incentive to avoid early commitment and reduce competition.

H3b: Late bidding occurs more frequently in voluntary-pay auctions than in winner-pay auctions.

Effect of propensity to pay on bid pledges.. In a voluntary-pay auction, a bid pledge is equal to the highest bid by a bidder. When people make ethical choices in hypothetical scenarios with no commitment mechanism, they pledge to make ethical choices that they do not adhere to in incentivized scenarios (FeldmanHall et al. 2012). Likewise, bidders who have a lower propensity to pay (upon losing) are expected to bid (pledge) higher in voluntary-pay auctions, because if they do not win the auction they are likely to default on the losing-bidder payment.

H4: Bidders with a higher propensity to pay tend to have a lower willingness to pay in voluntary-pay auctions than bidders with a lower propensity to pay.

Bid pledges in auction format. Drawing on economic theory (Isaac, Pevnitskaya, and Salmon 2010), we predict that for both charity and noncharity settings, both willingness to pay and the winning bid in the voluntary-pay auction are lower than in the winner-pay format. The intuition is that in the voluntary-pay format, bidders have to allow for the probability of losing the auction and still paying their bids, so bidders will not bid up to their valuations.

H5: Bid pledges are lower in voluntary-pay auctions than in winner-pay auctions.

Auction revenue. Current theory posits that in charity settings, revenues will be higher with all-pay auctions than with winner-pay auctions, given exogenous entry and full compliance from nonwinning bidders (Engers and McManus 2007; Goeree et al. 2005). The basic intuition is that in a winner-pay charity auction, a bidder who outbids a competitor forgoes a positive externality associated with the competing bidder’s contribution. In contrast, in all-pay charity auctions, competing bidders pay irrespective of whether they win, so the issue of forgoing competitors’ contributions by outbidding them does not arise. This result has a positive effect on revenue in all-pay charity auctions, despite the expected adverse effect on bidder entry. We expect that this difference in revenue will be preserved or be even greater in the voluntary-pay auction, because payment by losing bidders is not enforced. Bidders with low propensity to pay may drive up prices, because losing bidders will be less concerned about paying, as the money goes to charity.

H6: Total revenue in voluntary-pay auctions is higher than in winner-pay auctions.

Of importance in the analysis is the rate of compliance. Study 1 tests different appeals for compliance in voluntary-pay charity auctions. Specifically, we explore motives and boundary conditions for compliance behavior based on internal and external triggers of social norms.

We report on an experiment involving 125 Amazon Mechanical Turk participants in five treatments. The experiment is a 1 + 2 · 2 design with the following conditions: benchmark, negative internal, negative external, positive internal, and positive external. Appendix A shows the scenarios used. The benchmark condition included only the nonitalicized text. (None of the text was in italics when shown to participants—italics are for this exposition only.) The square brackets contain the manipulated text in the 2 · 2 conditions. The manipulations enable us to examine appeals to internal moral code versus external social pressure for compliance (Trevino 1986).

For each manipulation, we elicited the highest bid for each of the two formats of winner-pay and voluntary-pay.

We expected bidders to pay far higher amounts in the winner-pay auction, but an internal validity check is still necessary. One relevant construct for comparison of manipulations is the difference in the highest bids between winner-pay and voluntary-pay.

The primary issues are compliance and format choice, and particularly the trade-off between the two. If we compel people to pay—either legally (not an option here) or socially (which is investigated here)—then they will pay, but that comes at the cost of reduced entry and more cautious bidding. We observe this particularly for the negative internal and positive internal conditions, which are characterized by very high compliance but low choice of the voluntary-pay auctions (Table 3). The positive internal condition has the added disadvantage of a large drop (25%) in willingness to pay in the voluntary-pay format relative to the benchmark. Low entry combined with low bids mean that the higher compliance cannot possibly compensate ina terms of revenue. An attempted manipulation through positive appeals to internal norms would not have been effective.

The external comparisons provide the best of both worlds. They increase compliance even more than internal appeals but also raise preference for the voluntary-pay auction. In the case of positive external appeal, the appeal resulted in higher willingness to pay. In summary:

1. Negative and positive external social pressure both result in a significantly higher likelihood of paying than internal social pressure.
2. Internal social pressure—negative or positive—improves the likelihood of paying over the benchmark of no social pressure.
3. External negative or positive social pressure changes format choice in favor of voluntary-pay, indicating that decision makers derive value from public acknowledgment.
4. Internal pressure—negative or positive—relative to the benchmark setting improves the likelihood of paying but does not significantly change format preference, perhaps because it confers no additional benefit to subjects beyond the benchmark.
5. The positive internal pressure condition has the highest willingness-to-pay difference between formats, with willingness to pay in the voluntary-pay format being the lowest of all formats by nearly $100. It is possible that bidders perceive that format as a donation scheme. Thus, although positive internal pressure increases compliance, it does so at a drop of approximately 40% in revenues. Negative internal pressure is a close second in terms of willingness-topay differences between formats.

Overall, we find that social pressure (whether internal or external, negative or positive) increases payment compliance compared with the benchmark without social pressure (t = 1.74, p = .042),3 providing strong support for H1a. We also find support for H1b that social pressure decreases participation in the voluntary-pay format, though only for (both positive and negative) external pressure (t = 1.83, p = .036) and not for internal pressure (t = .06, p = .48). Finally, we find no statistical support for H1c’s proposition that social pressure has a greater negative influence on willingness to pay in voluntary-pay auctions than in winner-pay auctions, for either internal pressure (t = .87, p = .19) or external pressure (t = .10, p = .46). We test this hypothesis through the difference in willingness to pay between the winner-pay and voluntary-pay auctions. Although we find a larger difference for positive and negative internal pressure, consistent with H1c, this difference is not statistically significant.

In summary, the conjecture that one can “guilt” participants into paying is correct, but the result may be a loss of revenues. External pressure is the exception, possibly because of conditionally higher conferred benefits such as higher social status.

Our data for Studies 2 and 3 come from a local nonprofit auction website in a midsized metropolitan area in North America with a population greater than one million. At the time of the study, the site had roughly 9,000 registered members and is the same site as discussed in Haruvy, Popkowski Leszczyc, and Ma (2014). Bidders ( 1) are notified of auction events through emails to the site’s user base, ( 2) are registered users, and ( 3) have been exposed to both charity and noncharity ascending-bid auctions. Voluntary-pay auctions are introduced at the time of the study and represent only the second time bidders had been exposed to voluntary-pay auctions on this particular platform.

TABLE: TABLE 3 Results of Appeals for Compliance Behavior based on Internal and External Triggers of Social Norms

We conducted a field study comparing voluntary-pay and winner-pay auctions that were run at different times. The study controlled for self-selection on the bases of auction format (no choice of auction format) and products. We randomized the products sold. That is, prior to the auction, bidders did not know what products were in the auction.

We tested three auction formats in this order: ( 1) winner-pay regular auctions, ( 2) winner-pay charity auctions, and ( 3) voluntary-pay charity auctions. All treatments were identical with respect to items and differed only in auction format. For each format, we sold an identical batch consisting of 75 different products, for a total of 225 auctions (three identical replicates for the 75 products). Web Appendix A provides a detailed summary of the products sold.

Importantly, at any time, all 75 products were sold using a single auction format, and subjects were not exposed to the competing format within a week of the treatment. Recruitment did not mention the format and the products to be sold, and subjects found out about both only when they logged in to the auction site. They were given no indication that the same products would be sold in the future using a different format. All 75 auctions within a batch or format ran for four days. Auctions started at approximately 10 P.M. and ended at 10 P.M. on the last day with a 30-second interval between individual auctions.

One day before the start of the auctions, all registered members received anemail announcing the upcoming auctions. In contrast to Study 3, they received no details about the auctions or the products sold. Details about the auction format (rules for the voluntary-pay auctions and payment-rule description) and the charity component (if present) were displayed on the front page of the auction website and in all individual auctions just above the bid box.

Each auction consisted of a picture and a product description. All auctions were ascending-bid auctions with a proxy bidding system, similar to eBay. Bidding started at $.01, and all items were sold to the highest bidder. An important difference from eBay auctions is that bidders may submit either proxy bids or jump bids. With a proxy bid, the computer bids on behalf of the bidder incrementally up to his or her specified maximum willingness to pay. A jump bid specifies an amount to which the current high bid will jump. A bidder may also submit an incremental bid, in which the amount of the jump is equal to the minimum required bid increment. For estimation purposes, we include only proxy bids that are decisions by bidders, not computerized bids generated by the proxy bidding machine.4 The winning bidders paid for their items when they came to collect them at a local UPS store. The store does not sell any of the products sold in this study. In addition, all losing bidders in the voluntary-pay auctions were sent an email with a PayPal money request for the total of their nonwinning high bids for all auctions in which they participated (including a detailed calculation of the total). Importantly, the payments from losing bidders were collected separately from those of winning bidders. Thus, bidders could collect and pay for items they had won but could refuse to pay for the other amounts they had pledged as nonwinning bidders.

In total, 108 unique bidders participated in Study 2. Eighty-nine bidders participated in the winner-pay format, and of these only ten bidders participated in both voluntary-pay and winner-pay auctions, indicating that very little overlap in auction format occurred between bidders. In contrast, in Study 3, 116 (96) bidders participated in the charity (noncharity) winner-pay format and 58 (43) bidders crossed formats. Thus, whereas in Study 2 nearly no crossing occurred, in Study 3 roughly half of the participants crossed between formats. This is appropriate because Study 2 was intended to compare behavior between formats, whereas Study 3 was designed to examine bidding strategy while accounting for the interaction between bidding and format choice.

Study 3 compared winner-pay and voluntary-pay in a simultaneous choice setting. Investigation into the simultaneous choice setting has a dual purpose. First, the literature on the all-pay format has been adamant that comparison between formats should permit endogenous entry—that is, bidders should be permitted to opt out of an auction format. Study of endogenous entry requires running auctions for both formats simultaneously. Second, by examining choices between auctions of different formats, we can infer preference over formats based on choice.

Study 3, setting A: Simultaneous charity auctions. All events consisted of auctions for books, DVDs, games, household items, computer and electronic products, and other items (Web Appendix B summarizes the products sold). In charity auctions, a written announcement appeared at the bottom of the auction description, just above the bid box, stating that the donation would be made to the local United Way campaign. In addition, the charity component and the auction format were described in an email to all registered members.

The event included 221 pairs of identical product auctions for a total of 442 auctions over a period of five days. A pair always had one voluntary-pay auction matched with one winner-pay auction. All auctions lasted one day, starting between 8:00 P.M. and 9:00 P.M. and ending the next day between 8:00 P.M. and 9:00 P.M. All other aspects of the auctions were identical, including the text (explanation and example) at the bottom of voluntary-pay auction descriptions.

All registered members received an email about the upcoming auctions two days before the start of the auctions. The email provided details about the duration, the types of products sold, and the different auction formats, including the charity component and the charity involved. Similar to Study 2, the payment-rule description (winner-pay or voluntary-pay) was prominently displayed on the front page of the website listing the auctions and just above the bid box in all individual auctions. All other aspects of the study, including the platform, the type of auctions (ascending-bid auctions), and processing of the winning bidders and all nonwinning bidders in the voluntary-pay auctions, were identical to those described for Study 2.

Study 3, setting B: Simultaneous noncharity auctions. We conducted a similar event to that of Setting A with 466 auctions (233 pairs of identical auctions), but without the charitable component. Again, auctions were run in simultaneous pairs, in which one auction is in a winner-pay format and the other a voluntary-pay format for the same product. The study was conducted one year after the charity event (the temporal distance minimizes order effects wherein experiences carry over to the next event), and over a six-day period (excluding the weekend). All other aspects were identical to those in setting A. The products sold were similar, although not identical, to those in the charity auctions (Web Appendix B summarizes the products sold).

The purpose of having two simultaneous auction formats was to allow bidders to choose between them, and from these choices to infer preference. In the noncharity condition, 44.79% (SE = 5.10%) of the bidders moved between formats. In the charity condition, the corresponding number is 54.72% (SE = 4.86%). Thus, a reasonable assumption is that most bidders consider both formats. We find that restricting the analysis to only those subjects observed to move between formats does not change the results. We provide the regression results for all subjects and results with the more restricted population in Web Appendix C.

Study 2. Summary statistics related to auction outcome and bidder participation for Study 2 are summarized in Table 4. We report average winning bid, revenue, average number of bidders per auction, total number of unique bidders in the format, total number of switchers, revenue from winners, revenue from losers, revenue committed, revenue collected from losers, and total revenues. We report these findings for all three treatments of Study 2 with two-sample t-tests reported for comparison between pairs of formats for winning bid, revenue, bidders, and bids per bidder. To compare the revenues and other value-related variables, we normalized these variables by dividing them by the retail value. All tests are based on the normalized variables.

H2a posits that bidders with lower propensity to pay are more likely to enter or bid in voluntary-pay auctions than in winner-pay auctions. This expectation is tested in Study 1 and Study 3, but not in Study 2, because bidders do not choose the format.

H2b posits that voluntary-pay auctions have fewer entrants than winner-pay auctions. Table 4 shows that the number of bidders is smaller in the voluntary-pay format than in the winner-pay auction in the charity setting (t = 7.57, p < .001). In the charity setting, the winner-pay auctions had 4.41 bidders versus 2.52 bidders in voluntary-pay auctions. This finding strongly supports H2b.

H3a posits that more jump bidding will occur in voluntary-pay auctions than in winner-pay auctions. Recall our assertion that jump bids have an important deterrence potential in the voluntary-pay auction, owing to costly commitment, but do not have a similar potential in winner-pay auctions because those do not involve costly commitment. We find that of 614 final bids in the voluntary-pay auctions, 50.3% (SE = 3.7%) involved a jump bid. Of the 189 bids in the winner-pay auctions, 42.5% (SE = 2.0%) involved a jump bid. This difference is significant, based on a one-sided t-test (t = 1.88, p = .03), in support of H3a.

We find that 65.2% (SE = 1.9%) of final bids in the winner-pay auctions are late bids (during the last hour of the auction). In comparison, 63.5% (SE = 3.5%) of final bids in the voluntary-pay auctions are late bids. Thus, while last-hour bids are clearly the majority of final bids (and this is likely a strategic choice), we find no support for H3b in the non-simultaneous auctions. That is, we do not observe a higher incidence of late bids in the voluntary-pay auctions (t = .42, p = .68).

H4 asserts that bidders with a higher propensity to pay in voluntary-pay auctions have a lower willingness to pay. As with H2a, propensity is a latent construct. It appears in our discussion of model estimation. However, we have supporting model-free evidence for H4, because maximum willingness to pay in charity auctions for noncompliant bidders is $10.35 versus $5.82 for compliant bidders (t = 2.92, p = .004).

H5 asserts that bid pledges are lower in voluntary-pay auctions than in winner-pay auctions. Results from Table 4 show that the winning bid is $13.51 in charity winner-pay auctions and $6.76 in charity voluntary-pay auctions (p = .006). Thus, H5 is strongly supported.

H6 pertains to revenue comparisons. In Table 4, we see the auction outcomes for the two formats. The total combined pledged bids from the winning and losing bidders is higher in voluntary-pay ($1,352.40) than revenue in winner-pay ($1,013.06) auctions—a 33.5% improvement in revenue. The pairwise t-test is significant (t(74) = 2.34, p = .022). In the voluntary-pay charity auctions, $686.49 of $845.16, or 81.23% of the amount pledged by losing bidders, was collected, although this constituted merely 50.76% of the number of pledges by the losers. This constitutes 88.27% of the total pledged amount, including winners and losers, which is remarkable.

TABLE: TABLE 4 Summary Statistics for Study 2

Study 3. In Study 3, the formats were running concurrently, and therefore, we cannot infer a manipulation effect. Nevertheless, we can look at t-test comparisons over bidders and auctions with the caveat that these are not manipulation checks but are rather comparisons for completion in light of the results of Study 2. We provide a fully specified econometric model subsequently. H2a posits that bidders with lower propensity to pay are more likely to enter voluntary-pay auctions than winner-pay auctions. This expectation involves a latent construct that requires a model (discussed subsequently). Nevertheless, we find that noncompliant bidders (nonpayers) placed more bids in voluntary-pay than in winner-pay charity auctions (2.70 vs. 2.37, respectively; t = 2.68, p = .01) and noncharity auctions (1.88 vs. 1.62, respectively; t = 2.83, p < .01), in support of H2a.

In support of H2b, we find that the number of bidders is smaller in the voluntary-pay format than in the winner-pay format in both the charity (t = 7.03, p < .001) and the noncharity (t = 12.95, p In opposition to H3a, we find that the rate of jump bids in voluntary-pay auctions is lower than in winner-pay auctions, in both charity (t = 4.40, p < .001) and noncharity settings (t = 1.85, p = .065). Although we found support for H3a in Study 2, we do not find it in Study 3.

To investigate the effectiveness of jump bidding as a deterrent to entry, we considered all large jump bids (10% of the item’s retail value). Results suggest that large, early jump bids increase the likelihood of winning in a voluntary-pay (vs. winner-pay) auction from 22.64% to 34.09% (t = 1.46, p = .07, one-tailed test) in charity auctions and from 13.64% to 38.10% in noncharity auctions (t = 2.52, p = .007, one-tailed test). This implies that early jump bids may be effective deterrents to entry in both charity and noncharity voluntary-pay auctions. H3b is concerned with late bidding. Because entry into voluntary-pay auctions is more costly, late bidding is predicted to be more prevalent in this auction format. We observe a significantly higher degree of late bidding in voluntary-pay auctions. Specifically, in the charity setting, looking at the last bid for each bidder, we observe 32.3% late bids. This result is in contrast to 15.8% late bids in the winner-pay auctions. This result is significant at p < .01, providing support for H3b.

In the noncharity setting, we observe 20.01% late bidding in the voluntary-pay and 14.41% late bidding in the winner-pay (t = 4.39, p < .001) formats. This result provides strong support for H3b. Note that we did not find support for H3b in Study 2. However, in Study 3 bidders could choose which auction to bid in and, thus, select the winner-pay format. Bidders in Study 2 did not have this option, and more bidders entered early because bid levels were still relatively low.

Consistent with H4, we find that bidders with a higher propensity to pay in voluntary-pay auctions have a lower willingness to pay. In particular, for noncompliant versus compliant bidders willingness to pay in charity auctions was $8.76 versus $5.20 (t = 2.09, p = .037) and in noncharity auctions was $14.22 versus $4.90 (t = 6.34, p < .01). In support of H5, winning bids are significantly lower in voluntary-pay auctions than in winner-pay auctions. The winning bid in the charity winner-pay auction is $20.19 compared with $15.44 in the charity voluntary-pay auction (p < .001), and the winning bid is $13.43 in noncharity winner-pay auctions compared with $7.50 in noncharity voluntary-pay auctions (p < .001).

H6 predicts that revenue is higher in the voluntary-pay auction than in the winner-pay auction. The total combined pledged bids from the winning ($3,413.11) and losing ($4,427.95) bidders is $7,841.06 in voluntary-pay auctions, compared with $4,462.51 in winner-pay charity auctions; voluntary-pay auctions thus had 75.71% more bids pledged (t = 3.16, p = .002). In noncharity auctions, combined pledged bids from the winning ($1,698.58) and losing ($2,002.28) bidders is $3,704.46, versus $3,131.05 for winner-pay auctions (when normalized by retail price, revenue from the voluntary-pay format is slightly and insignificantly lower = .33, p = .745).5 Thus, we find higher revenues for the voluntary-pay format in the charity setting but not in the noncharity setting.

However, not all pledges from losing bidders are collected. In the voluntary-pay charity auctions, 48.58% of the amount pledged by the losers was collected, whereas in the noncharity auctions, 44.00% was collected. Overall, the revenue collected for voluntary-pay auctions is 24.69% higher than the total revenue obtained in the winner-pay auctions for the charity setting ($5,564.24 vs. $4,462.51). In contrast, in the noncharity setting, voluntary-pay revenue is 17.61% lower than winner-pay revenue ($2,579.76 vs. $3,131.05). Thus, the collected revenues provide support for H6, because revenue in voluntary-pay charity auctions is higher than that in winner-pay charity auctions. We find the opposite in the noncharity setting because revenue is higher for winner-pay than for voluntary-pay auctions.

Overall, results are highly consistent for Studies 2 and 3. The only difference is between jump bidding (H3a) and late bidding (H3b), because bidders have the option to bid in an alternative format in Study 3.

So far, we have compared aggregate statistics between formats. We next examine whether individual bidding strategies are consistent with theory—in particular, whether they employ strategies that suggest they view bids in the voluntary-pay format as implying commitment. Our focus is on two potential strategies expected to be effective in the voluntary-pay auction: early jump bidding and late incremental bidding. We expect these strategies to be more prevalent in the voluntary-pay auction if bidders perceive their bids as involving commitment—something bidders in the winner-pay auction do not have. We found model-free evidence, reported in Table 4, to suggest that these strategies are employed differently in the voluntary-pay format, but ultimately the strategies are process-related constructs that are driven by process-related variables. Without a joint model of strategic bidding that controls for dynamically changing explanatory variables as well as the relationship between the decision variables, we cannot correctly identify the role of commitment in the voluntary-pay format.

We employ a four-component model similar to that of Park and Bradlow (2005). The components capture the four strategic considerations of the bidder in the voluntary-pay format: auction choice, time to bid, jump bid, and propensity to pay. For the simultaneous design in Study 3, we estimate the full four-component model, while for the sequential design in Study 2 the model reduces to three components as auction choice is not present.

Endogeneity. When format choice is present (Study 3), jump bidding, time to bid, and propensity to pay link in the model to the propensity of auction choice. This type of endogeneity is a sample selection problem. In the Heckman approach (Heckman 1979), the inverse Mills ratio (IMR) —a transformation of the predicted selection propensity—serves as an explanatory variable in the outcome equation.

The preference for the voluntary-pay format is estimated first, followed by the other three decisions. This sequence allows the propensity to choose the voluntary-pay format to be incorporated into the other decisions through the IMR. We allow for bidder-specific random effects correlated across decisions.

Component 1: Auction choice. Each bidder chooses between two simultaneous auctions for identical items. Choice 1 in the pair is the voluntary-pay auction and choice 2 is the winner-pay auction. We use a latent utility model to capture auction preferences. This model implies that the choice of auction format is a choice between two utilities (Haruvy and Popkowski Leszczyc 2010). Given the binary choice, only the difference between the utilities is identified. The “difference” always refers to the voluntary-pay minus the winner-pay auctions. The utility difference between the voluntary-pay and winner-pay choices in auction pair I for bid k by bidder j is denoted DUijk and is defined as DUijk = b0, charityCharityi, k + b0, noncharity

LagPricei, k denotes the previous price. DLagPricei, k in the choice regression captures the difference in lag price on auction choice. DLagCumNoBiddersi, k captures the difference in competition between the auctions based on the cumulative number of bidders at the time of the previous bid. DLagJumpBidi, k is the amount of the previous bid increment, differenced between the two simultaneous auctions.

Lag$Committedi, k is the amount a bidder has committed in the voluntary-pay auction for auction pair i. LagTimeElapsedi, k is the time elapsed in the auction in seconds since the time of the previous bid. DLagFrenzy is the difference between the extent of frenzy for the two auction types, defined as the lag cumulative total bids divided by elapsed time (Dholakia, Basuroy, and Soltysinski 2002). Finally, Categories 1–3 are three dummy variables that account for heterogeneity in the utility across different product categories, partly addressing self-selection related to product choice.

The implied probability of bidder j choosing the voluntary-pay auction over the winner-pay auction in bid k of auction pair I follows a logistic distribution as follows:

We define the joint likelihood for bidder j’s format choices lchoicej as the product of all PrðChoiceijkÞ for bidder j over all auction pairs I in which bidder j participated, and bids k by bidder j.

Component 2: Time to bid. Let tijk and tijk-1 denote the timing of bid k and bid k - 1 by bidder j in auction pair i. We assume that the time between bids, Timeijk = tij k - tijk-1 of bidder j, who places the kth bid in auction pair i, follows a Weibull distribution, with a probability density function:

LagPricei, k denotes the previous price and therefore captures the effect of price dynamic on the timing of bids. LagCumNoBiddersi, k and LagFrenzyi, k are both important variables that influence the timing of bids. LagTimeElapsedVoluntary-pay is the interaction between LagTimeElapsed and an indicator variable for the voluntary-pay format. The parameter h1j represents an individual-specific error term for bidder j, such that E(h1j) = 0 and variance s2 h1 > 0. Finally, e1ijk represents the error term. We can then define the joint likelihood for bidder j’s timing choices lTime j as the product of all fðTimeijk; lij kÞ for bidder j over all auction pairs I inwhich bidder j participated, and all bids k by bidder j.

Component 3: Jump bid. We model jump bids by the amount the bidder bids over the previous highest bid minus the minimum bid increment. Jump bidding can serve as a deterrent in all-pay auctions but not in winner-pay auctions (Dekel, Jackson, andWolinsky 2007).Let I here denote auction I rather than auction pair. Let –i denote the other auction in the pair. LetVoluntary-Payi, k be an indicator function (dummy variable) that takes the value of 1 if auction I follows the voluntary-pay format, and 0 otherwise.

After controlling for two separate intercepts for charity and noncharity, the next three explanatory variables in the equation denote the lag prices in the current auction, separately for charity and noncharity, and in the competing auction. LagCumNoBiddersi, k is critical in that it measures how responsive jump bids are to the presence of competitors. If jump bids are a deterrent, as we claim, we expect to see a positive and significant coefficient for this variable, indicating that bidders facing a competitive environment use jump bids to eliminate some of the competition. The variable Lag$Committedi, k is exactly the same as the variable by the same name in the “Component 1: Auction Choice” subsection. The next four terms pertain to the time elapsed in the auction and its interaction with auction format and whether it is a charity auction. Frenzyi, k is an important explanatory variable because it could be responsible for some jump-bidding activity. When auctions are slow (corresponding to low frenzy), bidders may incur significant opportunity or monitoring costs, and the jump bid size becomes a strategic decision, with bidders choosing larger jump bids (Kwasnica and Katok 2007).

We add an IMR regressor to account for self-selection separately for charity and noncharity auctions. The IMR is used as a regressor in the estimation of Equation 5 per Heckman (1979). Equation 8 indicates the joint estimation and points out the IMR.

The error e3ijk is normal i.i.d. censored at 0, with standard deviation s3. The individual random intercept h3j has a mean of 0 and variance s2 h3 > 0. The correlation between the individual effect for jump amount and the time to bid is denoted by rh13. Let Iijk be an indicator function of whether a positive jump was observed. The likelihood of a jump of a specific magnitude, accounting for the censoring at 0, is

Wedefine the joint likelihood for bidder j’s jumpchoices ljump j as the product of all PrðJumpijkÞ for bidder j over all auction pairs I in which j participated and all bids k submitted by bidder j.

Component 4: Propensity to pay. We investigate a latent bidder’s propensity to pay, whereby higher propensity to pay means higher probability of observing payment on a losing bid. The propensities are unobserved, but we do observe who paid a losing bid. The propensity to pay clearly depends on what happened in the auction—both process and outcome. Wemodel propensity to pay, whichwe capture as an individual intercept in the utility from the decision to pay plus the explanatory variables specified in Equation 7; the IMR coming from the choice regression in Stage 1 of the Heckman two-step procedure highlighted previously; and a random component.

Because we have already usedUto denote choice utility, we use V to denote utility from paying a losing bid. The probability of paying a losing final bid (one final bid per bidder per auction) in auction I by bidder j is PrðPayijÞ = Fð-DVijÞ, where

Charityi, k is a dummy variable indicating whether an auction is a charity auction. The IMR is a regressor in Equation 7 per

Heckman (1979); however, because it is just an intermediate step, we do not explicitly state it in Equation 7. Equation 8 indicates where IMRis used in the joint estimation. Wecan then define the likelihood of bidder j’s payment choices lpay j as the product of all PrðPayijÞ for bidder j over all auction pairs I in which bidder j participated.

The parameter h4j represents an individual-specific error term for bidder j, such that Eðh4jÞ = 0 across bidders. The individual bidder’s h4j remains the same for all decisions made by individual j, and h4j’s variance is denoted by s2 h4 > 0. The term e4ij represents the error term for propensity to pay and follows a normal distribution. Note the absence of subscript k, denoting the bid, on all terms except the last price. This absence occurs because the decision to pay is at the auction level and not at the bid level.

Joint estimation. We estimate jump bid and bid timing jointly through a maximum likelihood involving jointly distributed errors for the two decisions with a bivariate normal distribution. The choice decision is estimated by itself in Stage 1 of the Heckman two-step procedure as a standalone. Jump bid and time are linked to it through the IMR, which appears in Equation 8.

Equation 8 shows the joint likelihood of the four components. We define H1j as the vector of bidder-specific intercepts for both the propensity-to-pay and the format-choice equations. WedefineH2j as the vector of bidder-specific intercepts for both the jump-bid and the time-to-bid equations. Hj follows a multivariate normal distribution with mean 0 and variances S. Let fðH, SÞ be themultivariate normal distribution of parameter vector H, conditional on the covariance matrix S. Using the bidder-specific likelihoods defined after each equation, we write the joint likelihood function for all bidders in Equation 8 as follows:

Equation 8 is maximized to estimate the coefficients, which requires two steps. In the first step, we get the IMR from the choice equation, and in the second step, we recover the second half after the IMR is estimated.

Our discussion focuses on the results of the four-component model from Study 3, which are provided in Table 5. We compare these results with the results from Study 2. Results for auction choice. The auction choice component captures the choice of the voluntary-pay auction as the dependent variable. The significant negative coefficient for LagPrice means that as prices evolve over time, bidders are more likely to choose the auction with the lower price. The significant negative coefficients for DLagPrice in both the charity and noncharity settings suggest that as the difference between the current prices or high bids becomes larger, bidders are more likely to choose the auction with the lower price.

TABLE: TABLE 5 Results for Study 3: Simultaneous Winner-Pay Versus Voluntary-Pay Auctions in a Charity and Noncharity Setting

The positive significant coefficient for DLagCumNoBidders in noncharity auctions suggests herd behavior, consistent with competitive arousal. This is consistent with the positive signifi-cant coefficient for DLagFrenzy, suggesting that bidders are more likely to choose an auction in response to an increase in Lag-Frenzy. The positive significant coefficient for DLagJumpBid in both noncharity and charity auctions means that bidders are more likely to react to a large competing jump bid.

We find a positive significant coefficient for Lag$Committed in both charity and noncharity auctions. This means that a bidder finds the voluntary-pay format more attractive if (s)he has already made a commitment in that format (which needsto be paid whether (s)he wins or loses). The positive significant coefficient for Lag-TimeElapsed in charity and noncharity auctions means that bidders are more likely to bid as more time has elapsed in the auction.

Results for time-to-bid component. Time between consecutive bids is inversely related to the lambda parameter. Thus, a positive coefficient for any explanatory variable implies more concentrated bidding. We see a positive significant effect for LagPrice for noncharity auctions, suggesting that bids are more concentrated as price rises. This finding is consistent with our finding of strategic late bidding. We observe the opposite pattern—a negative coefficient on LagPrice—for charity auctions, wherein bidders with charitable motives have an incentive to bid earlier to push up the bid to help raise money for the organization (Haruvy and Popkowski Leszczyc 2009; Popkowski Leszczyc and Rothkopf 2010). The positive coefficient on cumulative bidders indicates that bids are more concentrated in auctions with more bidders. A positive coefficient for LagFrenzy suggests that an increase in LagFrenzy leads to more concentrated bidding. The positive coefficient for LagTimeElapsed is consistent with late bidding. The positive coefficient for LagTimeElapsedVoluntary-Pay indicates that bidders tend to wait to bid in voluntary-pay auctions. This result provides support for H5 for charity, where H5 predicts more late bidding in voluntary-pay auctions than in winner-pay auctions.

Results for the jump bid component. The jump bid component of the model considers the magnitude of a jump bid. The positive coefficient for LagPrice_charity shows that bidders place a greater jump bid when the price is high, while the significant positive coefficient for LagCompetingPrice indicates that jump bids are influenced by the competing auction’s price. The positive coefficient may mean that a high price in the competing auction indicates that the price in the current auction is too low—thus the jump.

The positive significant coefficients on LagCumNoBidders for both charity and noncharity auctions indicates that bidders in both types of auctions are more likely to place greater jump bids when the auction is more competitive. The coefficient for Lag$Committed in the voluntary-pay auction is significant and positive in both charity and noncharity auctions, indicating more aggressive bidding by bidders who have already committed more money in an auction. Although the coefficient is significant, we observe half the magnitude of aggressive bidding in charity auctions, in which bidders may be less concerned with the amount they have committed because the money goes to charity.

The negative effect for LagTimeElapsed in both charity and noncharity auctions suggests that the more time that has elapsed, the less likely bidders are to place higher jump bids in these auctions. However, the positive interaction between time elapsed and voluntary-pay auction format in both charity and noncharity auctions suggests that bidders are more likely to place a higher jump bid later on in a voluntary-pay auction, implying that bidders use jump bidding to try to win the auction. Finally, we observe differences in jump bidding across product categories, with less jump bidding for category 1 (DVDs, games, and books).

Results for propensity-to-pay component. For the decision to pay, we find that losing bidders are more likely to pay in charity auctions, in more competitive auctions (frenzy), and when the final ending price is low. We further observe that bidders are more likely to pay for products in category 1 but less likely for products in category 2. Finally, IMR has a positive effect, suggesting that bidders who have a higher likelihood to bid in voluntary-pay auctions are more likely to pay. This result suggests a strong relationship between bidders who self-select into the voluntary-pay format and bidders’ propensity to pay for a losing bid.

Because of the endogenous choice of auction format in Study 3, we did not expect identical results between Studies 2 and 3. However, as we expected, we find that the ranking across conditions is consistent for winning bids, pledged revenues, collected revenues, compliance rate, number of bidders, and number of bids per bidder.

The rankings of collected revenues and compliance rates are comparable across the two studies. In Study 2, total revenue is $871.06 for noncharity winner-pay auctions, $1,013.06 for charity winner-pay auctions, and $1,193.73 for charity voluntary-pay auctions. All revenues are for the same set of 75 auctions run at different times, with zero overlap between formats. While the percentage difference between collected revenues in the winner-pay and voluntary-pay formats is somewhat smaller in Study 3 (17.83%) than in Study 2

(24.69%), both differences indicate that voluntary-pay auctions produce greater revenues than winner-pay. Although the percentage of collected revenues is slightly higher in the non-simultaneous setting than in the simultaneous setting—51% versus 49%—we consider these patterns remarkably robust.

Statistics for the paying and nonpaying losers in the voluntary-pay auctions show that paying losers bid less aggressively than nonpaying losers ($6.94 vs. $9.33; t = 2.92, p =.004), very similar to the results reported for Study 1 ($5.24 vs. $8.76; t = 2.09, p = .037). Finally, we look at the extent of jump bidding and the timing of bids. We find that the extent of jump bidding was similar in magnitude, although the rank order was not consistent across conditions for Study 2 versus Study 3 for winner-pay charity (46.62% vs. 47.51%), winner-pay non-charity (44.04% vs. 36.95%), and voluntary-pay charity

(56.91% vs. 41.87%). Furthermore, we find a greater percentage of early jump bids in voluntary-pay charity in Study 2 (5.79%) than in Study 3 (16.66%), suggesting that when a winner-pay format is also present, bidders avoid making a commitment in the voluntary-pay auction early on and tend to bid later on, when the price difference between the auctions becomes larger.

The extent of snipe bidding had the same rank order across conditions for Study 2 versus Study 3 for winner-pay charity (9.49% vs. 13.87%), winner-pay noncharity (13.99% vs. 15.09%), and voluntary-pay charity (22.73% vs. 41.24%). However, the magnitude of snipe bidding was significantly higher in Study 3 than in Study 2 for the voluntary-pay auction. This result is consistent with a lower number of early jump bids in Study 3.

For Study 2 we estimate a three-component model including the decisions on time to bid, jump bid, and propensity to pay. The explanatory variables are the same as for Study 3. However, any explanatory variables pertaining to the competing auction (e.g., LagCompetingPrice) are not applicable, nor are any variables pertaining to the missing treatment of voluntary-pay noncharity (e.g., Lag$Committed_noncharity; LagTimeElapsedVoluntary-Pay_noncharity). Furthermore, the main treatment intercepts (charity, noncharity) cannot be identified separately from voluntary-pay, because noncharity is present only for winner-pay. Estimation results appear in Web Appendix D.

Table 5’s comparison of the results from the sequential setting of Study 2 with those from the simultaneous setting of Study 3 reveals three major differences:

1. In the time-to-bid component, all signs and significance levels are preserved except for the reaction to past prices, which we term “strategic late bidding.” Specifically, in Study 3, we find that lag price in the noncharity setting has a significant positive effect—meaning late concentrated bidding in response to high prices. In the charity setting in Study 3, we find the opposite pattern. In Study 2, in contrast, we found no significant effect of past prices on the timing of bids in either the charity or noncharity setting, possibly owing to the absence of choice between formats reducing the benefit of strategic late bidding.
2. In Study 2, in the jump-bid component, only the time variables are significant. This finding is different from Study 3, in which bidders are less likely to place higher jump bids late in voluntary-pay auctions, probably because bidders have no alternative outlet in which to bid.
3. In the propensity-to-pay component, charity is not included in the sequential model (it is confounded with the intercept since the voluntary-pay was run only in a charity setting). In contrast to Study 3, bidders are no more likely to pay when the ending price is low or when auctions are more competitive.

In summary, the main insight from the three-component model is that the relationships indicated in Study 3 are largely preserved. The same was the case for the hypotheses discussed previously. The purpose of the four-component model was to extract revealed preferences from the observed format choices, which Study 2 cannot do. We controlled for self-selection owing to auction format, but self-selection on product choice in Study 3 may persist. That is, people may have self-selected themselves into Study 3 on the basis of product preferences. However, the three-component exercise served as a robustness check and ensured that there would be no reversed patterns or exploding magnitudes in one study versus the other. We find that the patterns are robust, suggesting that self-selection bias owing to auction format is not a concern.

We studied compliance behavior and revenue implications in winner-pay and voluntary-pay auctions in charity and noncharity settings. Study 1 was an experiment that tested different social appeals (using internal and external pressure) for compliance. We find that although we can effectively apply social pressure to compel bidders to pay, the unintended—yet intuitive—consequences of that action may be reduced entry and lower bids.

In addition, we conduct two field studies to study voluntary-pay auctions and payment compliance in a real-world setting. In particular, we focused on revenue implications for voluntary-pay versus winner-pay auctions in charity and noncharity settings as well as bidding strategies such as jump bidding and strategic late bidding.

Overall, results showed that in the charity setting, in which social responsibility was high, the voluntary-pay auction resulted in higher collected revenues for the seller even though commitments were not binding. In the noncharity setting, in which social responsibility was lower, we found higher revenues for winner-pay auctions. The difference in the relative success of voluntary-pay auctions between charity and non-charity settings can be attributed to three factors. First, bidders are more likely to participate in charity voluntary-pay auctions than in noncharity voluntary-pay auctions. Second, bidders are willing to bid higher in charity settings than noncharity settings. Third, higher revenues can be collected from losing bidders in charity settings.

While payment noncompliance in auctions is occasionally addressed in the literature (e.g., Bruce, Haruvy, and Rao 2004), for the most part it is ignored as nonconsequential. In this work, we provide a compelling demonstration that noncompliance is highly consequential. Not only does compliance vary between settings, but its dependence on the setting changes the profitability of possible formats relative to one another. Non-compliant bidders self-select into formats in which they can take advantage of the trust of the format to extract higher surplus. We found important differences in bidding behavior between losers who reneged on their commitment versus those who did not. In particular, nonpaying losers tended to bid more aggressively, placing more and higher bids. Although these bidders reneged, discouraging them may be unwise, as they play an important role in increasing prices and revenues in voluntary-pay auctions. Alternatively, we find that different appeals may be effective to increase compliance. In particular, external social pressure in the form of public acknowledgment of payments results in a significantly higher likelihood of payment than internal social pressure and improves format choice to favor voluntary-pay auctions.

Second, our research has broad and important implications for bidding strategies. We focused on two strategies in voluntary-pay auctions: early jump bidding and late incremental bidding. We expected these strategies to be more effective in voluntary-pay auctions because bidders face costly commitments—something bidders in winner-pay auctions do not have. Late bidding is helpful because it allows bidders to defer commitment and thus lower their risk. Accordingly, when bidders have a choice between formats, we observed more late bidding in voluntary-pay auctions (for both charity and noncharity settings).

The interaction between time to bid and jump bidding is a key strategic consideration for bidders (Haruvy, Popkowski Leszczyc, and Ma 2014; Kwasnica and Katok 2007) and is of paramount importance. Bidders with a high cost of time will place higher jump bids (Kwasnica and Katok 2007). Thus, jump bidding coincides with longer bidding intervals. Our empirical demonstration is coupled with a methodological implementation that gives researchers and practitioners a simple way to implement this insight in an estimation framework that can demonstrably apply in a field setting.

This article has important implications for marketing managers. Our findings augment the growing stream of empirical auction studies and corporate social responsibility in marketing and contribute behavioral insights.

Improving fundraising capabilities. Our findings add to the increased focus on fundraising in the corporate social responsibility literature. We find that combining an auction with a donation to charity may result in increased revenues—a finding with wide-reaching implications. While all-pay auctions have long been advocated as advantageous in raising funds for social causes, in practice the feasibility of collecting on such auction formats has hindered their wide adoption. We showed that perfect compliance is not essential, and a similar format—the voluntary-pay auction—is also advantageous in charitable settings.

The importance of compliance incentives for format selection. The finding that voluntary-pay auctions substantially increase collected revenue in charity settings while resulting in lower collected revenue in noncharity settings suggests that in the absence of strong contractual enforcement mechanisms, voluntary-pay auctions tend to be better suited to settings with moderate to high social responsibility. When selecting an auction format, or any two possible pricing formats, managers should ensure that they are accounting for differential compliance rates between the auction formats they are considering. If the two formats are running concurrently, managers should consider whether buyers with different levels of compliance propensity might self-select among formats in a manner that distorts incentives.

Compliance by losing bidders in voluntary-pay auctions. While managers may apply social pressure to comply people to pay, they need to take care that this will not result in reduced entry and more cautious bidding. To increase compliance, they may want to use positive external pressure (e.g., by publicly posting losing bidders who paid). Finally, while it may be possible to identified and screened out low-compliance bidders, doing so is not clearly advantageous, because noncompliant bidders bid up prices.

In the long run, bidders might sort themselves into charity or noncharity, winner-pay, or voluntary-pay auctions according to preferences and expected payoffs. Clearly, our results would be weaker if noncompliant bidders entered en masse into the voluntary-pay charity auction. Although this is not the case with the population we study, it is an important boundary condition. Two factors should be considered: the proportion of compliant bidders that would enter the voluntary-pay auction (charity or otherwise) and the bidding aggression of noncompliant bidders. Any drop in the proportion of compliant bidders would have to be offset by an increase in bidding aggressiveness of non-compliant bidders to keep voluntary-pay auctions superior. To characterize this frontier, a controlled study would need to systematically vary the proportion of compliant and non-compliant bidders.

Experimental Conditions for Different Compliance Appeals

You are facing two possible auction formats:

Voluntary-pay auction: This is a special format where all bidders—including losing bidders—receive a bill for the amount of their highest bid (e.g., if you place several bids and your highest bid is $150, then whether you win or lose you will receive a bill for $150). We cannot force payment on losing bids but all payments by losing bidders will be donated to charity.

While we cannot force you to pay the amount of your bid if you lose, your compliance with the auction rules is important toward raising funds for the Children’s Hospital Foundation for the purpose of helping young children with cancer. [Negative External: Losing bidders who fail to pay their highest bids as requested will be listed on the foundation’s website as unmet pledges.] [Negative Internal: Losing bidders who fail to pay their highest bids as requested cause immeasurable harm to our fundraising efforts and to the children who rely on them.] [Positive External: Losing bidders who pay their highest bids as requested will be listed on the foundation’s website as donors.] [Positive Internal: Losing bidders who pay their highest bids as requested greatly benefit our fundraising efforts and the children who rely on them.]

Regular winner-pay auction: Only the winning bidder is asked to pay—and receives the item he or she won. Losing bidders pay nothing. The proceeds are donated to the same charity as the voluntary-pay auction.

1 http://pages.ebay.com/help/sell/unpaid-items.html.

• 2 Posner (1977, p. 411) explains the legal principle that “promises are not enforceable unless supported by ‘consideration.’ This means, roughly speaking, that a promise will not create an enforceable contract unless it is made in exchange for something of value—goods, money, another promise, or whatever.” Posner lays out potential legal exceptions to this rule.
• 3 The p-values are based on one-sided t-tests for all directional comparisons, including this one and the ones that follow.
• 4 As an example, suppose the bidding increment is $.25, and Bidder A bids $7 by proxy (= zero jump), while the required minimum bid is $3. Thus, the actual displayed bid by this bidder is $3. Bidder B observes this bid of $3, chooses proxy and bids $4. The proxy tool will immediately outbid him at $4.25 on behalf of Bidder A. If Bidder B now chooses jump and bids again at $5 (the indicated minimum bid is $4.50 and so the jump = $.50), the proxy by Bidder A will still outbid him at $5.25. The resulting outcome is identical to a situation in which Bidder B had used a proxy at $5, but the observation here is counted as a jump of $.50, and a use of the proxy would have been a jump of $0. Bidder B finally selects a jump bid at $9. The bid now shows as $9—not as $7.25, because Bidder B did not choose a proxy bid.

5Voluntary-pay charity auction revenue from winners is 76.49% ($3,413.11/$4,462.51) of that from the winner-pay auction. For the noncharity auctions, revenue from winners is only 54.25% ($1,698.58/$3,131.05).

DIAGRAM: FIGURE 1 Conceptual Model with Hypotheses for Bidding in Voluntary-Pay Versus Winner-Pay Auction Format

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A Theories-in-Use Approach to Building Marketing Theory 

This article's objective is to inspire and provide guidance on the development of marketing knowledge based on the theories-in-use (TIU) approach. The authors begin with a description of the TIU approach and compare it with other inductive and deductive research approaches. The benefits of engaging in TIU-based research are discussed, including the development of novel organic marketing theories and the opportunity to cocreate relevant marketing knowledge with practitioners. Next, they review criteria for selecting research questions that are particularly well-suited for examination with TIU-based research. This is followed by detailed suggestions for TIU research: focusing on developing new constructs, theoretical propositions (involving antecedents, moderators, and consequences), and arguments for justifying theoretical propositions. A discussion of TIU tradecraft skills, validity checks, and limitations follows. The authors close with a discussion of future theory-building opportunities using the TIU approach.

Keywords: building theory; grounded theory; theories-in-use; theory construction; theory development

The marketing discipline is at a crossroads ([28]; [47]). Marketing scholars can continue on the well-worn road of largely testing or extending theories by borrowing from allied disciplines, or we can challenge ourselves to make a significant difference in the lives of managers, public policy officials, and/or consumers. Our point of view is that this road less traveled necessitates deeply and richly exploring marketing topics from the perspectives of individuals (i.e., consumers, managers, and/or public policy officials) who are closest to the problem. This means leaving the comfortable confines of our faculty offices to explore, identify, and define new marketing concepts in their natural habitat.

Importantly, as we leave our offices to engage with individuals closest to the problem, we are not simply advocating recording, summarizing, and building rich descriptive narratives. While these narratives are valuable in their own right, we are advocating something more. Namely, we advocate constructing new-to-the-world marketing theories. It is widely acknowledged that theories launch the fundamental knowledge of a discipline ([49]) and are the building blocks for the maturation of a discipline. Articles whose primary contribution is based on proposing theories are generally viewed favorably ([60]). In fact, theoretical advances are critical to the development of marketing as a discipline ([30]). Not surprisingly, editors welcome new theories that are particular to the marketing discipline (see [33]). Against this background, our objective is to discuss an approach that is ideally suited to the development of theories in marketing: the "theories-in-use" (TIU) approach.

A TIU is a person's mental model of how things work in a particular context ([ 2]). As part of daily life, all individuals employ mental models ([66]). All stakeholders in marketing—among them managers, customers, employees, and public policy makers—have mental models that can be elicited by TIU research to surface interesting, novel theories and concepts that can advance both marketing practice and scholarship. Specifically, we argue that TIU is a natural approach for creating theories that are specific to marketing-related issues—what have been referred to as organic ([26]) or home-grown ([49]) theories. Organic marketing theories involve central constructs that are uniquely or primarily grounded in the marketing context rather than borrowed from other disciplines such as economics or psychology. In this regard, TIU has served as an approach for organic contributions to the marketing discipline by bringing to fore concepts such as service quality, market orientation, experiential consumption, customer solutions, and hybrid offerings.

More specifically, a TIU approach can help address three fundamental problems in our discipline. First, when we borrow from other fields, our own stakeholders' problems do not guide our research. Rather than allowing our own stakeholders' problems to guide us, we force-fit a theory or framework on which to base our research. The result is that we are not building a discipline-based body of knowledge. This borrowing approach is certainly one reason that marketing scholarship is losing touch with the practice of marketing ([28]; [47]). Second, borrowing constrains us because we restrict ourselves to what is already known, thereby hampering our search for novel and interesting phenomena. Third, when using abstract theoretical constructs from other fields, we lessen our ability to communicate with our stakeholders in a vocabulary they understand. It is much easier to advance the practice of marketing if one speaks the same language as practitioners than it is to introduce an entirely new glossary of terms.

Paradoxically, only a (relatively) small number of TIU articles have been published to date. This is surprising because TIU articles not only are published in our most respected journals but have won major awards (e.g., Shelby D. Hunt/Harold H. Maynard Award, Sheth Foundation/ Journal of Marketing Award), have established subfields of study within the discipline (e.g., service quality, market orientation), and have been a key catalyst for endowed chair appointments at some of the best business schools. As Table 1 notes, three of the top ten articles in Journal of Marketing are TIU articles. Despite this clear discipline and career impact, few researchers pursue TIU research.

Graph

Table 1. Citations of Top Ten Articles Published in the Journal of Marketing.

1 Notes: Articles in bold employ a TIU approach. WOS = Web of Science Index; GS = Google Scholar. Citation counts gathered on October 4, 2019.

Accordingly, this article aims to inspire and support development of knowledge based on TIU among marketing stakeholders. To achieve this objective, we organize this article as follows. We begin with a definition of the TIU approach. In this section, we compare and contrast TIU with other grounded theory methods and deductive research methods for knowledge development. Following this, we discuss key benefits of engaging in TIU-based research. With this foundation in mind, we turn to the practice of TIU research in the field. We divide this practice discussion into two sections: one that overviews the "basics" of TIU research and one that provides insight on the advanced tradecraft of the practice. As with any method, one must be able to judge "good and bad" practice; thus, we then turn to an assessment of rigor in TIU. This is followed by a discussion of the limitations of the approach. We conclude with suggestions for future research.

[66] note that individuals' TIU may be envisioned as a set of "if-then" relationships among actions and outcomes. For example, an advertising manager's TIU may include the proposition that if she associates her brand with an important social cause, then millennial consumers may be more likely to buy her brand. People's TIU may also include complex if-then relationships. For example, a marketer's TIU may include the idea that a firm's customer-centricity improves its profitability, but an increase in customer centricity beyond a certain level adversely affects firm profitability because it is too costly. That is, there is an inverted U-shaped relationship between customer centricity and firm profitability.

At its core, the theory construction process involves developing novel if-then propositions. In contrast, the theory-testing process involves empirically assessing the validity of previously developed propositions. While the two processes and their aims are distinct, they potentially can be interrelated. For instance, a theory-testing effort may reveal unexpected findings, which may lead to the construction of new theory to account for the findings. Our focus in this article is on the theory construction process for developing new theory about a phenomenon.

[ 2] coined the term TIU to refer to individuals' mental models of the world that guide their deliberate behavior. They contrasted the concept with "espoused theories" that refer to the mental models individuals claim or purport to have. While overlap may exist between individuals' TIU and their espoused theories, often these two types of theory differ. For instance, individuals may be unable to articulate parts of their TIU that are tacit. More often still, defensive reasoning mindsets develop that discourage sharing revealing insights ([ 1]).

The TIU approach has unique characteristics that bear highlighting. The approach involves soliciting from study participants—the theory holders—the ideas they feel are important and how they are linked to one another. The emerging set of interrelated constructs, regardless of how complete or incomplete they may be as theories, become a researcher's starting point for harvesting constructs, propositions, and arguments. Researchers, however, are not simply passive recorders of participants' thinking. They use their viewing lenses to elicit, evaluate, abstract and extend what they "hear" from theory holders included in the study ([63]). The TIU approach relies on one-on-one participant conversations and elicits theories from a relatively small number of participants (often 15–25).

The TIU approach is also unique in that it is a partnership that allows for the cocreation of a theory. Participants are treated as active partners in the theory development process, allowing for the presence of implicit and explicit causal thinking among them about the ideas they consider important. Researchers may then draw on other sources of insight they have acquired about the topic to modify the ultimate constructs' abstraction levels and causal connections among them to develop theoretical propositions. Said differently, a TIU approach assumes that the theory holders being interviewed have theories that researchers can uncover and extend using other sources of insight. This is what makes a TIU approach a partnership. It is grounded in two different mindsets—that of the researcher and the interviewees—each focused on theory.

A TIU approach becomes an even stronger partnership when researchers convene representative stakeholders including some original study participants to critique and discuss the researcher's tentative formal theory. In this way, two mindsets, the researcher's and the theory holders', are formally brought to bear on the topic. A new and better theory is likely to be created. This is less likely or even unlikely to occur with other approaches falling under the rubric of grounded theory construction.

In general, the theory construction process is inductive in nature. Scholars collect various types of data through means such as unobtrusive observations, secondary data, and participant interviews. They reflect on these data to identify patterns and create new theory. The theory so developed is termed "grounded theory" to indicate that it is created from observations and data pertaining to a phenomenon on the ground ([ 7]; [12]; [15]; [54]).

We provide an overview of three formal approaches for building grounded theory in Table 2: TIU, case studies and ethnography. The TIU approach relies on elicitation of theories held by individuals with proximity to the problem (e.g., [ 4]). Case studies are in-depth studies of one or a few comparative cases (e.g., [ 5]; [14]). Ethnographies are in-depth studies of a phenomenon aimed at describing its meaning/significance to a group's members and the reasons underlying the meaning/significance (e.g., [16]).[ 6] Importantly, researchers can use these approaches in tandem; for example, a researcher using the case study method can fruitfully include a TIU approach for making comparisons across cases.

Graph

Table 2. TIU and Related Approaches.

2 Notes: N.A. = not applicable.

The theory construction process, however, can also be deductive in nature. For instance, in theoretical modeling, researchers set up models (settings/scenarios) with different characteristics and derive implications of the models for the behaviors of participants in the model (e.g., firms, salespeople, consumers). These behaviors are then linked to the (differing) characteristics of the different models (generally across articles) to construct new theory ([34]).

In many instances, researchers review the literature, see gaps or conflicts, and propose new theory, often by introducing a moderator construct or a new explanation stimulated by their own experiences or derived from extant research. This process can be inductive or deductive in nature. For instance, when researchers combine knowledge about a phenomenon in the literature with their personal experiences related to the phenomenon to develop new theory, it is more akin to an inductive process. In contrast, when researchers put two or more findings/assertions in the literature together to derive a new theory, the process is deductive in nature.

Table 2 shows prominent inductive and deductive approaches for theory construction and summarizes key differences among them with respect to six facets: purpose, researcher mindset, research process, data collection method, sample selection, and sample size/depth. As the table shows, a major difference between the inductive and deductive approaches is that whereas inductive approaches start with data pertaining to a phenomenon of interest, deductive approaches start with models (settings/scenarios) or theories and work through their implications. A related difference is that whereas a researcher's mindset in inductive approaches is one of exploration and hunting (seeking and processing data in quest of theoretical insights) for constructs and theories inherent but hidden or as yet unarticulated in data, the researcher's mindset in deductive approaches is one of setting up models that are sufficiently realistic yet tractable.

As with any research approach, TIU suits certain research questions better than others. We identify major motivations for engaging in TIU research, whether as a stand-alone approach or in combination with other approaches. We find that TIU research is particularly valuable when scholars want to ( 1) construct organic marketing theories, especially about new and emerging phenomena; ( 2) extend extant perspectives and address ambiguities; or ( 3) guide future empirical efforts. In this section, we take a closer look at these three motivations.

Constructing organic theories is important to any discipline because organic theories offer unique insights not available outside of the discipline and thus provide good reasons for the discipline's existence as an academic field. Unfortunately, marketing scholars tend to borrow more heavily from other fields than those fields recognize and borrow from marketing ([ 6], [46]). However, the development of organic marketing theories—such as that on service quality, market orientation, and experiential consumption—has influenced other fields, and articles on these topics often receive thousands of citations. As noted by [52], p. 171), "Clearly, the academic market recognizes the value of homegrown constructs and theories."

Because the TIU approach takes advantage of marketing practitioners' or consumers' experience and knowledge about the marketing setting, it is especially well suited to identifying and defining important constructs that reflect the practical world of marketing, including antecedents and consequences of marketing phenomena. Consider two examples: service quality (see [40]]) and market orientation (see [27]]). Both sets of authors used the TIU approach to develop their pioneering conceptual frameworks. They were able to do so in part because managers had developed practices that offered useful grist for the development of ideas on each topic. Each conceptual framework has prompted significant empirical work and paved the way for substantial research streams on services and market orientation over many years, and is among the top ten cited articles in the Journal of Marketing (see Table 1). Table 3 provides an illustrative set of articles that develop organic theory using the TIU approach.

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Table 3. Examples of Organic Theoretical Contributions Using TIU Research.

Theories-in-use-based research is also useful when the aim is to extend extant perspectives about a construct. For example, while research on customer solutions in business markets mushroomed in the early 2000s, solutions were viewed only from the suppliers' perspective. Missing from the discussion was a customer-centric perspective on solution offerings. Using a TIU approach, [56] provided a view of solutions from the customers' perspective, which extended the supplier view of solutions. Similarly, when conflicting theoretical perspectives exist on a novel construct, a TIU approach can help researchers better understand when and why one theoretical perspective may be preferable to the other. Relatedly, the TIU approach can bring precision and clarity when there is ambiguity surrounding constructs and/or nomological net of relationships among constructs. The approach has fewer advantages when working with well-defined constructs where the nomological net has been mapped out comprehensively in prior research.

Theories-in-use research is often the ideal foundation for empirical efforts. As an example, [40] used a TIU approach to understand the meaning of service quality from the perspective of consumers, employees, and executives, which guided two major empirical efforts that produced multiple publications.

The first effort resulted in identifying ten dimensions of perceived service quality gleaned from eight group interviews. The researchers termed the first set of empirical efforts SERVQUAL, a multidimensional scale for measuring consumer perceptions and expectations of service quality ([43], [44], [45]; [40], [41]). Development of scales rests on sound conceptual foundations, and insights of specifics provided by practitioners in a TIU approach helped inform these operationalizations. When queried about the need for expectations in the measure, the authors followed up with another TIU study ([69]).

The second effort resulted in the gaps model of service quality, which linked performance by various entities (e.g., employees, channels) to the gap between consumer expectations and perceptions of service quality, and the communication and control processes within organizations that produce these gaps ([68]). A follow-up study empirically examined these variables to identify the most important in each of the four gaps and the relative importance of the four gaps themselves (Parasuraman, Berry and Zeithaml 1991a). Finally, the researchers empirically linked perceived service quality to intentions to examine the behavioral consequences of perceived service quality ([70]).

The TIU approach is best suited for addressing research questions/issues that are broad and deep, and for which we do not have good answers. Research participants should be selected for their knowledgeability about the questions/issues and willingness to share their knowledge and experiences with the researcher. In general, a typical research project requires 15–25 participants selected in successive phases. The knowledge/experience required of the participants in each phase becomes clearer as the research progresses and theoretical ideas come into sharper focus. Importantly, the researcher should have a very strong interest in the research questions/issues and should have good general knowledge related to them. This enables the researcher to listen carefully to participants, ask probing questions, challenge participants when appropriate, and engage with participants in a flexible way—adapting the questions asked to the idiosyncratic knowledge of individual participants and to the learnings from prior participants in the TIU study.

Figure 1 provides an overview of the TIU research process. The process typically begins with a focal research construct to be examined in the research (e.g., market orientation, service quality).[ 7] If the construct is not well defined, the research begins by clarifying and defining the core construct. This may take several iterations and feedback loops based on participant conversations (see right-hand side of Figure 1). If the construct is well defined, the research moves to the stage of developing propositions and their associated arguments. The propositions can include antecedents, consequences, mediators, and or moderators. After a few conversations, the researcher begins to formulate tentative propositions that may be assessed on basic screening criteria related to the plausibility and strength of reasoning. After multiple propositions are developed, they must also pass higher-order assessments that relate to the overall contribution of the set of propositions (see Figure 1). These pertain to whether the collective set of ideas adds to the existing literature. As Figure 1 shows, there are numerous feedback loops illustrating the continual iteration and refinement of the conceptual structure.

Graph: Figure 1. The TIU research process: an approximation.Notes: Foundational tests = Are propositions plausible, and aligned with definitions and arguments? Advanced tests = Are propositions interesting, substantially informative, and hang together (have one or a few common themes)?

The aim in TIU research is not to simply transcribe participants' statements. Rather, it is to review data across participants, look for common themes/ideas in the specifics provided by participants, and abstract commonalities to broader constructs/variables that form the building blocks of an emergent theory. A researcher strives not to present a particular participant's TIU but rather to present a theory reflective of the beliefs and actions of multiple participants, including variables and propositions extrapolated from those beliefs and actions (see [65]] and [61], [62]]).[ 8]

Researchers should develop a brief conversation guide that lists a few broad questions they wish to ask participants, along with related probes and follow-up questions.[ 9] If permitted, each conversation should be recorded, notes should be taken during the conversation, and a memo to oneself written immediately following the conversation as to how it adds to prior ideas and points to future lines of inquiry. A researcher returns to these recordings, notes, and memos as a theory begins to take shape and uses them to provide substantiating evidence in the research report.

The purpose of conversations with participants is to tap into their tacit and explicit knowledge and beliefs about the research problem/questions of interest to the researcher: ( 1) construct development, ( 2) proposition development, and/or ( 3) argument development. In this section, we describe the nature of the conversations needed for each of these three research problems. We first provide basic guidelines on the TIU research approach, followed by more advanced guidelines for addressing the three research problems. In the next two subsections, we discuss tradecraft related to the fieldwork and identify important checks for rigor in the research.

We suggest starting a participant conversation by introducing the topic and segueing into asking what the phenomenon (construct) means to the participant and others familiar to the participant. For an illustrative conversation flow and set of questions, see Table 4. The researcher must ask for specific examples of varying levels of the phenomenon and how it is similar to or different from other proximal constructs (for specific questions, see the top section of Table 5). The researcher should periodically check whether the tentative definitions (s)he is forming are consistent with participants' understanding of the phenomenon. Participant conversations flow unpredictably and generate a lot of ideas and stories, many of which may not relate to the research problem of defining the construct of interest. The researcher must, therefore, continually try to refocus the conversation on the construct (and away from, for example, its antecedents or consequences or just irrelevant information).

Graph

Table 4. Construct Hunting: A Suggested Conversation Flow for TIU Research.

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Table 5. Key Questions/Probes for Building Theories Using the TIU Approach.

For example, [27] started participant conversations by asking, "What does the term market orientation mean to you?" Some of the responses were along the following lines: "It's all about customer need satisfaction,"[10] "You have to know what competition you are up against," "It means your research and development (R&D) is in touch with what's going on in the market," and so on. The researchers formed a tentative idea of the construct's domain from these responses. For example, these responses suggested that the construct was about delivering customer satisfaction in the face of competition, and that R&D is somehow involved in the process.

A follow-up probe, "Tell me a little about your activities that reflect a market orientation," elicited numerous responses. They included "We keep our eyes on the customer and competitors," "We put the customer at the center of everything we do," "We make sure people in one function know what people in other functions are doing," "We reward people for providing exceptional service," and so on. These comments suggest that the construct involves knowing customers and competitors, everyone in the company focusing on customers, and each function knowing what the other functions are doing. Note that the last quote is indeterminate as to whether it belongs to the construct's domain or is an antecedent of the (yet to be precisely defined) construct. Follow-up probes might ask, "Can you tell me how one function finds out what the others are doing?" and "What exactly do you do to know how the consumer environment is changing?" to clarify these questions.

After a few of these conversations, the researcher begins to identify commonalities across the participant observations and to abstract them to a higher level. For example, while some participants indicated that they sent out customer surveys, others relied on syndicated data. Yet others visited customers personally. However, the commonality here is that of the generation of customer intelligence through different methods. This led to the development of the idea that market orientation involves, in part, intelligence generation about customers. Subsequent conversations and ongoing reflections led to the eventual definition of market orientation as organization-wide generation, dissemination, and responsiveness to market intelligence.

A researcher's focus here is the development of if-then propositions that aim to identify a phenomenon's antecedents, consequences, mediators, and/or moderators of the phenomenon's effects. At one level, this is relatively straightforward—the researcher asks participants questions such as "Can you give me examples of actions you took to increase X (the phenomenon)?," "In your opinion, what happens when X increases?," "Can you recall instances in which X didn't lead to that?," and "What accounts for the unexpected results?" However, participants frequently identify antecedents that reflect the core construct itself or are too proximal to the core construct to be of theoretical interest. For example, when asked to indicate why some organizations are not very market oriented, one participant said, "It's because they fail to give customers what they want." Note that this is a part of the market orientation construct, not its antecedent.

The types of questions that the researcher asks should be based on the research goal (see Table 5). For example, if the research goal is to link construct X to novel outcomes, the researcher may ask, "What are the benefits of doing X?," "Were there any surprises or unexpected outcomes of doing X?," or "Did increasing the level of X lead to outcomes that contradict conventional wisdom about X?"

As the conversations progress, the researcher forms a relatively clear (albeit tentative) proposition that X leads to Y. At this point, the researcher can assess the proposition by asking questions directly related to the proposition. For example, the researcher may say, "My last interviewee believes that X leads to Y. What is your view?" or "My last interviewee found that X leads to Y. What is your reaction?" This is particularly useful for propositions that include abstract constructs developed by the researcher. If the level of abstraction is too high, subsequent participants are likely to indicate that the proposition(s) is questionable.

In addition to developing if-then and "if-then-except-when" theoretical propositions, a researcher must also provide plausible arguments or justifications for the propositions. Argument development involves probing participants for the reasons they hold their if-then beliefs. Thus, a researcher may ask participants, "Why do you believe X leads to Y?" or "Why do you expect M to strengthen the effect of X on Y?" Developing an argument may also involve listening to the reasons offered by participants and identifying one or more mediators of the effect of X on Y. As in the case of developing theoretical propositions, the challenges here pertain to appropriate level of abstraction as well as to maintaining consistency with the evolving definitions of the core construct and the antecedent, consequence, or moderator variables involved.

A key aspect of theory construction using a TIU approach is the process of abstraction from the raw data surfaced in the course of participant conversations. Abstraction involves considering two or more elements (e.g., words, phrases, ideas in one or more sentences) in raw data (e.g., transcriptions of participant conversations), pooling the elements into a higher-order category or construct, and giving it a label (i.e., name/term). Such a construct is of a higher order (i.e., is more abstract) than the elements in the sense that it captures the essential information in the two or more elements but excludes some of their details. [ 7] refer to the general process of identifying and categorizing distinct elements in the data as "open coding."

A researcher may use one or more of several approaches for abstracting from the elements (e.g., words, phrases) contained in the data obtained from participant conversations. We discuss three approaches. In the first approach, a researcher examines the data within and across participant conversations and notices that they contain several elements that have different meanings but all seem to be subsets of one of the elements in the data. In this case, the latter element is of a high-order (i.e., is more abstract), and the researcher may consider it a candidate construct for his or her theory. The abstraction process here is one of identifying elements that are all a part of a broader, more abstract element and treating all as the latter element for the purpose of theory construction. Importantly, this calls for the researcher to actively seek out such interrelationships among the elements to identify them. For example, participants may provide the following statements to a researcher to indicate that their respective firms are market oriented: "We survey customers to find out their needs and wants," "Our company does a lot of market research every quarter," "Our salespeople ask customers how we can serve them better," and "We generate intelligence about our markets." In this case, the process of abstraction involves observing that the italicized elements in the first three statements are subsets of the italicized element in the fourth statement and thus suggests using the construct of "market intelligence generation" in subsequent theory construction efforts.

In the second approach, a researcher examines the data within and across participants and notices that they contain elements that likely co-occur (or covary). That is, when one element is present (or is at a high level), another element is also likely to be present (or at a high level). The researcher pools these elements into a higher-order category or construct and, if needed, gives it a label/name. For example, one or more participants may describe customer reactions to exceptional service in restaurants as follows: "They feel valued as customers," "Their eyes come alive," "They smile, and thank the waitpersons," and "They leave big tips." Each of the italicized elements likely occurs when the other elements also occur. As such, the researcher may pool them into a higher-order category or construct of customer satisfaction and use it for subsequent theory construction.

In the third approach, the researcher examines the data within and across participants and notices that they contain elements (e.g., words, phrases) that are neither subsets of one of the elements (approach 1) nor do they necessarily co-occur or covary (approach 2). Rather, they appear to be different facets/dimensions/aspects of a broader concept or idea. The researcher pools these elements into the higher-order category or construct and, if needed, gives it a label/name. For example, participants may describe outcomes of investing in market research as "Market research helps us get a bigger piece of the market," "It brings in more revenue," and "It costs money, but in the end, we save money because we don't try to be all things to all customers." The italicized elements are not subsets of one of them and often do not covary, but each is an indicator of the broader concept of how well a firm is performing. As such, the researcher may pool them into a higher-order category or construct of firm performance for use in subsequent theory construction.

Importantly, as a construct's meaning begins to form, a researcher must take care that the construct's domain is not too narrow or too broad. If it is too narrow, it is too specific and limits the generalizability of the theory. If it is too broad, its components may not all relate to other constructs (potential antecedents or consequences) in a similar manner. This becomes clearer as the construct's antecedents and consequences emerge in the course of participant conversations. Importantly, as a construct's meaning begins to emerge, the researcher must ascertain whether it is truly capturing a distinct phenomenon, one not reflected by other known constructs (especially those already discussed in the literature). For example, when asked whether they thought market orientation and customer orientation were the same thing or different, most participants pointed out that market orientation was a broader construct in that it focused on customers and other influences on them, whereas customer orientation focused exclusively on customers. Upon reflection, it became clear that the two would have somewhat different antecedents (e.g., company systems that base rewards on customer satisfaction vs. those that base them on broader metrics such as market share and profitability).

After developing constructs, a researcher links them to develop tentative theoretical propositions, stimulated by participants' TIU elicited in course of participant conversations. The general process of linking two or more concepts with each other is referred to as "axial coding" ([ 7]). There are two main challenges in developing propositions that identify antecedent, consequence, mediator, and moderator variables.

First, when the core construct/phenomenon is yet to be defined precisely, the emerging antecedents, consequences, mediators and moderators need to be identified and defined in conjunction with the core construct in a way that the resulting propositions make sense. For example, when a participant in the market orientation research was asked, "Why do some firms fail to give customers what they want?," he indicated, "Well, they are afraid of changing what they have done for many years. They feel safe doing the tried and tested." A further "why" probe led to "Because they are afraid they will be pulled up by the management if they do something different and it bombs." A few more probes later led to the more interesting revelation that an organization's employees may fail to provide customers the offerings they need because of the fear of being punished by their managers who themselves are concerned about being punished by a risk-averse top management. This led to the identification of "top management risk aversion" as an antecedent of market orientation. Note that "top management risk aversion" is not a part of the core construct and is a relatively abstract, novel construct, and the proposition makes sense if market orientation is defined in part as responding to customers' changing needs.

Second, the propositions developed should ideally be novel (i.e., not documented in the literature) and interesting (i.e., not obvious but useful). Such propositions often challenge conventional wisdom, identify conditions in which extant theory does not hold, or develop interesting nuances that lead to "aha!" moments for the readers. Frequently, however, participants offer input with little insight. For example, when asked why some firms are more market oriented than others, several participants indicated, "Firms that are market oriented are that way because they care," and "It takes hard work to be market oriented." These and many other ideas that emerged in the course of the conversations were either obvious or previously documented and, therefore, not pursued further. It is important for the researcher to continually ensure that the propositions (s)he is generating and retaining for further consideration are new to the literature, interesting, plausible, and of importance to some set of stakeholders.[12]

A straightforward way for a researcher to develop arguments to support a theoretical proposition is to ask participants why they believe (and perhaps why they do not believe) in a proposition. It is very important, however, for a researcher to critically evaluate the soundness of the reasoning before accepting it as plausible. The researcher may also develop theoretical propositions and arguments by connecting disparate ideas obtained from two or more participants. For example, one participant may note that doing A leads to Y, and another participant may suggest that doing X leads to A; putting these two assertions together would suggest the testable proposition that doing X leads to Y, the argument being that X leads to A, which in turn leads to Y.

Frequently, a researcher's goal is to construct a set of coherent theoretical propositions that collectively represent a substantial contribution to the literature. After generating a reasonably large number of theoretical propositions, a researcher should take stock of them with a view to selecting the ones that have one or a few common themes such that the selected set can be formalized in a parsimonious way. The researcher may group the constructs involved across propositions into broader categories, or identify one or a few common high-level arguments across propositions. The general process of choosing from among the theoretical ideas developed in a research process is referred to as selective coding ([ 7]).

In this section, we discuss key nuances of the TIU research process and offer suggestions that increase the likelihood of developing impactful new theory. Following this, we offer suggestions for crafting research papers.

As noted previously, the theory construction process entails collecting data from a few participant conversations and then interacting with the data to generate preliminary, tentative theory (constructs, propositions, and arguments). The tentative theory guides the researcher's focus in collecting data from subsequent participants. These data frequently augment the tentative theory and/or suggest its modification (e.g., revising constructs, changing their abstraction levels, adding propositions, developing new arguments). The resulting theory, in turn, guides subsequent data collection, and so on, until a researcher is satisfied with the theory.

For example, say that a researcher is interested in constructing a theory of postrecession performance of firms. Drawing on data collected from the first few participants, the researcher constructs a tentative theory that a firm that increases its R&D spending during a recession enjoys higher market share after the recession. After a few more conversations, the researcher constructs another tentative theory that a firm that invests in operations to make them more efficient during a recession increases its profitability after the recession because it redirects slack resources during the recession to reducing ongoing operations costs. At this point, the researcher considers the elements "R&D spending" and "investments in operations" and abstracts them to a broader construct of capability building. Similarly, the researcher abstracts "market share" and "profitability" to a broader construct of firm performance. Using these constructs, the researcher constructs the proposition "The greater a firm's capability building during a recession, the greater the firm performance postrecession."

A researcher is not simply a passive ear. The maxim that data do not say anything—only managers or researchers do—applies to TIU research as much as it does to other methods ([64]). Theories-in-use approaches provide a special opportunity for researchers to exercise disciplined imagination and add unique value to an investigation. This occurs, for instance, when researchers listen carefully for what a participant is not saying (i.e., what potentially important ideas seem to be missing or understated by interviewees). For example, in an insight development project, managers had little to say about the important constraints placed on insight development by long-standing company policies.

Similarly, a researcher may develop a theoretical proposition that was not directly stated or derivable from participant data but still grounded in them. For instance, one participant may identify P as a new antecedent of a phenomenon, and another participant may identify M as a moderator of the effect of a different antecedent R. The two sets of ideas may lead the researcher to examine whether M may moderate the influence of P (in addition to that of R). A researcher also has an important role in adding value by explaining why certain findings are surprising, counterintuitive, or contrary to received wisdom on the topic.

When engaging with a participant, it is key for researchers to temporarily suspend their prior beliefs and tentative ideas developed in the course of previous participant conversations. This is not easy, but it is important to listen with an open mind, absorb the participant's ideas, and probe deeper into those that have the potential for generating new insights. Researchers can feign ignorance and ask a number of "why" questions even if they believe they know the answer: "Why do you say that?," "Why does it affect X?," "Why would doing X not be helpful in circumstance M?" As these questions continue, they can lead to interesting new insights.

As may be evident from the previous examples, participant conversations elicit considerable commentary. When listening to a participant's responses, the researcher should try to identify and define the abstract construct that reflects the detailed description provided by the participant. To the extent the researcher is successful in doing this, the theory construction task following the participant conversations becomes easier because a theory essentially is a set of interrelated constructs. It is helpful to record participant conversations as well as take notes during the conversations, which can be revisited in the course of developing construct definitions, theoretical propositions, and arguments.

It is sometimes more productive for a researcher to go where a participant's interest takes the conversation rather than strictly focus on the precise questions with which the researcher comes into the conversation. For instance, a participant may say something that may seem a bit odd or unrelated to the research questions. The researcher may be tempted to brush it aside to have a more "productive" conversation, but doing so may lead to missing out on potentially interesting and useful new ideas.

With each conversation a researcher learns a little more about the three components of the theory under development: construct definitions, theoretical propositions, and arguments. (S)he must relate these to those learned from earlier conversations up to that point. This provides greater confidence in similar ideas obtained in previous conversations. Ideas not previously elicited can be noted for further exploration in subsequent conversations. The researcher may also encounter ideas that are in conflict with established ideas. For example, some participants may indicate that R&D spending in recessions hurts performance, whereas others may believe that it helps performance. These may prove to be most interesting and need to be resolved (perhaps by identifying appropriate moderators) in subsequent participant conversations.

A researcher also tracks the components of a theory that are developing well as well as those that are "light" and need further exploration; (s)he then selects subsequent participants accordingly and engages in conversations that address those components. For example, after a few conversations with brand managers, a researcher interested in constructing a theory of brand love may learn more about the antecedents and consequences of brand love than about the moderators of its consequences. Thus, the researcher may focus more on surfacing moderators in subsequent participant conversations. At some point, researchers will recognize that continued collection and analysis of data is unlikely to yield new themes, categories, or substantive insights, a situation known as theoretical saturation.

As we know from research on cognitive biases, peoples' mental models can be deficient. For example, opinions and strongly held feelings have a way of surviving challenges from facts. Just because a participant expresses a particular story with conviction does not mean that it should be accepted by the researcher as factual. While it is a sincere expression of the participant's judgments, the story merits critical examination and possible correction or improvement ([23]; [51]; [55]).

It can be difficult to figure out the right "demarcation" between the "context" of a TIU study and the constructs studied. For example, a context may be business-to-business firms and a researcher may be exploring constructs X, Y, and Z. The business-to-business context, however, also has other constructs associated with it (e.g., direct sales force vs. channel partners, client concentration). Therefore, the researcher has a choice here: to study the context variables and include them in the theory, or to limit the theory to the study's context.

A researcher may substantiate claims about a construct's meaning and/or a theoretical proposition (along with its underlying logic) by indicating how several participant conversations reflected this. Providing direct quotes from one or two of them is a convincing way to accomplish this. These quotes provide a verbal lexicon and allow the reader the opportunity to develop an alternative formulation. However, as participants in a conversational mode frequently allude to multiple ideas in a single sentence or two, the researcher must portray quotes that clearly and unambiguously make the intended point.

Another emergent, value-added quality that can strengthen a paper is an answer to the question, "So what?" This question can be answered from both a researcher and marketing stakeholder standpoint. For example, the final construct network or mental model that represents consensus thinking among participants can be used as a playground for theory construction. The researcher may offer an additional map containing new constructs and their proposed relationships along with those already in the map. The changes in the map (i.e., the new constructs and their connections with others previously identified in the interviews) would represent the researcher's unique reflections about the data. This new bundle of related, testable propositions is a new theory that could guide future research and thinking.

The consensus map may also be a basis for helping marketing stakeholders think through its relevance to their positions. The researcher can offer "map management" suggestions to stakeholders. For instance, they might be encouraged to ask, "Which constructs should be emphasized or deemphasized in their situation? How might particular connections between constructs be weakened or strengthened? What new constructs may be added to the original consensus map network?" Essentially, questions like these help stakeholders shore up strengths in their thinking and compensate for limitations.

This section offers criteria for evaluating the rigor of a TIU-based study. Some of the criteria commonly used to evaluate studies include internal validity, external validity, and reliability (see [39]]). Several scholars, however, have long argued that these criteria are cast in a positivist tradition, and that different criteria should be used to evaluate interpretive research (e.g., [17]; [29]). Researchers have developed numerous criteria for evaluating interpretive research, some of which mirror the commonly used criteria of reliability and validity. Prominent among these are four criteria described by [17] and [29]: credibility, transferability, dependability and confirmability (see also [ 3]]; [ 9]]). These criteria are discussed by [19] and have been used in prior marketing research (e.g., [13]).

A TIU-based study shares aspects of the positivist as well as the interpretive traditions. It is positivist in that it aims to develop clear new causal associations about a phenomenon and interpretive in that it uses study participants' interpretations of the phenomenon. For this reason, we adapt the four criteria (credibility, transferability, dependability, and confirmability) for evaluating the rigor of TIU-based research and indicate tests researchers can use to demonstrate (and evaluate) the rigor of their new theories. Importantly, while these four criteria are useful, we suggest they need to be complemented by a fifth criterion—distinctiveness—that refers to the novelty of a theory's constructs and propositions (relative to extant literature). This criterion is central for evaluating TIU research whose aim is the construction of new theory. Table 6 summarizes these five criteria and how they may be used for evaluating TIU research.

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Table 6. Rigor in TIU Research.

Credibility is analogous to internal validity, and in the context of TIU-based research refers to the extent to which a new theory's if-then propositions are plausible.[13] This may be demonstrated by providing strong arguments to support the propositions. For this reason, we recommend probing participants for why they believe in their if-then propositions. Their responses (potentially combined with extant theories and findings in the literature) can be instrumental in constructing persuasive arguments for the new theory's if-then propositions. We also recommend asking participants range-spanning questions to encourage them to consider the full range of constructs involved (e.g., very high to very low; e.g., [27]). For example, if some participants indicate that strong loyalty programs lead to higher market shares, it is useful to ask subsequent participants (or the same participants later in course of the conversations) about the consequences of having weak loyalty programs along with the reasons for those consequences. If participants indicate that one of the consequences is low market share and they provide the same argument for it, documenting this information is likely to increase the theory's credibility. Finally, we recommend comparing across participants. To the extent multiple participants suggest the same theory, its credibility is enhanced.

Transferability is analogous to external validity, and in the context of TIU-based research, it refers to the extent to which a new theory's constructs and if-then propositions are valid in contexts not included in the data used to develop the theory. Researchers can increase confidence in the transferability of their new theory through appropriate theoretical sampling of participants in their studies. As a tentative theory emerges in the course of conversations with participants, researchers can select as the next set of participants those for whom the theory may not hold (e.g., participants in different types of firms, industries, geographic locations; participants with different experiences; e.g., [ 4]). To the extent the next wave of participants suggests the same emergent theory, it increases confidence in the transferability of the theory. If the subsequent participants suggest different theories, it would indicate the need for a resolution, generally through the incorporation of one or more moderators and/or inclusion of additional antecedents/consequences.

Dependability is analogous to reliability, and in the context of TIU-based research refers to the extent to which multiple researchers ("multiple human instruments" per [19], p. 241]) involved in a TIU study find the same constructs and if-then propositions from the same data.[14] This may be assessed through comparison across researchers. To the extent multiple researchers processing the same data converge on the same theory, its dependability is enhanced.

Confirmability is analogous to objectivity, and in the context of TIU-based research refers to the extent to which a new theory's constructs and if-then propositions can be independently certified as emerging from the data (rather than from researchers' predispositions, interests, and motivations). Researchers can demonstrate confirmability by documenting participant checks that are similar to member checks suggested by [29]. Researchers may present their emerging (as well as eventual/final) theory to TIU research participants and ask them whether it is consistent with their views (as well as invite comments/remarks).

Researchers can also demonstrate confirmability by documenting agreement between two or more independent judges (i.e., knowledgeable individuals who are not involved with the research) about the new theory's correspondence with the data used to develop it. For example, researchers using a TIU approach typically develop abstract constructs from specific data (instances, examples) provided by participants. In such cases, researchers can demonstrate confirmability through interjudge reliability. This involves researchers providing two or more judges the raw/verbatim data (or a random sampling thereof) and the names of their abstract constructs and having them code the raw/verbatim data into the constructs. Following this, interjudge agreement may be computed (e.g., using proportional reduction in loss proposed by [50]]). Similarly, researchers can demonstrate confirmability by documenting agreement between two or more independent judges asked to indicate the extent to which a theory's if-then propositions correspond to the data from which they were created. Researchers can also demonstrate confirmability by providing thick descriptions of their data (e.g., verbatim participant quotes) in their reports to enable readers of the theory to do a direct assessment of the extent to which theoretical constructs, propositions, and arguments advanced in the theories are consistent with the raw data used to construct them.

As noted previously, data from TIU research participants can stimulate a researcher to develop if-then propositions that were not cited or directly suggested by any of the participants. Deviations from the data provided by participants also arise when a researcher develops a theory incorporating constructs at different levels of abstraction than those stated by participants. In such cases, we caution against strict adherence to the confirmability criterion and instead suggest using theory credibility as the more important criterion. This is because the central purpose of using TIU for theory construction is to develop new theory that accurately explains a phenomenon of interest, not one that is an accurate restatement of data provided by participants.

Distinctiveness is analogous to discriminant validity, and in the context of TIU-based research, it refers to the extent to which a new theory's constructs and if-then propositions are different from existing constructs and if-then propositions in the literature. Because it is counterproductive to introduce new labels for existing constructs, it is important to ensure that new constructs in a theory refer to different phenomena than existing constructs. Construct distinctiveness may be demonstrated by definitional comparisons—comparing the proposed definition of a new construct with definitions of existing constructs that are closest to the meaning of the new construct. Proposition distinctiveness refers to the extent to which if-then propositions differ from theoretical propositions already available in the literature. Propositional distinctiveness may be demonstrated by documenting closely related existing propositions individually or in summary form and visually showing the differences between them and the new theory (e.g., in a table).

We argue that while all five criteria are useful for evaluating a new theory, the primary emphasis should be on credibility, transferability, and distinctiveness. Dependability and confirmability are good virtues, but not as pertinent as credibility, transferability, and distinctiveness. This is because the end goal of TIU research is the development of a new theory that can explain a phenomenon across multiple contexts; it is conceivable that researchers may develop such a theory even when it is somewhat lower on interresearcher reliability (dependability) and interjudge reliability (confirmability).

Importantly, the quality of a theory based on TIU of participants is likely to be influenced substantially by the quality of theories held by the participants. As noted previously, researchers should take care to sample participants who are likely to be knowledgeable about the phenomenon being studied and also willing to share their knowledge with the researchers. By documenting the participants' qualifications, researchers can engender greater confidence in the theories they develop using the TIU approach (see Table 7).

Graph

Table 7. Glossary of Terms Used.

Like all research approaches, TIU has limitations and challenges. First, TIU, as a technique used largely for theory construction, is not suited for theory testing. However, as we have shown, TIU can be a terrific setup for guiding downstream theory-testing efforts. Second, researchers often lack (but can still acquire) the requisite skill and experience needed for doing successful interviews with key informants. Using the recommendations in this article is a good start. Next, reading the TIU research delineated in our Tables 2 and 3 will help. Finally, practicing interviews with other researchers using TIU can prepare a researcher to conduct interviews with actual participants.

Third, TIU works only when informants have sufficient knowledge and experience. For relatively new phenomena (e.g., a firm operating as a platform as well as a supplier on the platform), participants are unlikely to have well-developed theories about their long-term effects and/or the conditions under which the effects are likely to be strong or weak. Participants in these situations may still espouse theories, but they are less likely to be the product of thoughtful processing of meaningful experience. An idea about a relatively unfamiliar issue could be an uncertain participant's guess as opposed to a highly relevant but newly discovered "aha."

As we have noted, the discipline of marketing is at the crossroads. Others have suggested that if we continue on our current trajectory, we will simply accelerate our path to irrelevance ([47]). One promising method to increase relevance to all stakeholders in the marketing system is a TIU approach. Relevant stakeholders may be managers aiming to improve practice, consumers aiming to enhance their consumption experiences, and/or policy makers aiming to improve society. In this section, we turn our attention to three specific areas of future research. The first two future research areas focus on direct applications of TIU. The first application area is non-domain-specific. Here, the emphasis is on identifying "meta issues" that can richly inform any subfield of marketing (e.g., when stakeholders disagree, when core assumptions underlying a body of work are questionable), whereas the second is focused is domain-specific (e.g., role of marketing in the firm, organic growth, digital transformation). The third category involves research on TIU as a method.

In this section, we consider research topics that could apply to any field or subfield of marketing. In a sense, these topics are "meta" questions that can guide researchers in selecting topics specific in their area of specialization. Next, we explore three such issues.

An assumption is a hypothesis that is taken for granted. A theory built on an assumption that is not fully explored may be incomplete or may even contain errors. The published literature often identifies and debates such assumptions. For example, the literature on market orientation currently has two dominant perspectives—one focusing on processing marketplace information ([27]) and the other focused on a market-oriented culture ([38]). However, both perspectives assume that understanding customer needs and putting customers at the "center of your business" is essential for success. An interesting question to be explored using a TIU approach would be "When do customer needs not matter?" or "Under what conditions should the customer not be at the center of the business?" The notion of building businesses around customer is at the heart of our discipline, yet it could be challenged by examining successful businesses that have taken a different approach. From a public policy perspective, there is an assumption that it is "always" best build a business around a customer; however, this assumption can also be reexamined. When do customer-oriented businesses increase consumer costs and lessen customer satisfaction?

A TIU approach can be very productive when a firm's or even industry's "theory" of its behavior in the marketplace is at odds with their customers' "theory" of the firm's or industry's intentions and actions. Comparing manager theories or maps of their actions with those customers hold about the same actions can help a firm or industry achieve a better alignment with its customer base. A contemporary example involves current viewpoints regarding the pricing of drugs in the pharmaceutical industry. Here there are conflicting views held by firms (e.g., high prices support the portfolio of R&D efforts, some of which work and others do not), policy makers (e.g., consumer affordability), and customers (e.g., price gouging).

A TIU approach may make clear where key stakeholders agree or disagree and what options exist that can foster agreement among them. These are common situations in the public health, political, and nonprofit marketing settings. Very few articles using a TIU approach exist in the public policy domain, yet many agencies' stakeholder interests are in conflict in that domain. For that reason, this is an especially promising domain for organic theory construction.

As we discussed previously, many sources exist to identify content domains suited to TIU-based research. These include Marketing Science Institute, industry-specific surveys of "hot topics," trade association agendas, public policy agencies' grant funding priorities, and the American Marketing Association. Many of these institutions also provide researchers with direct access to subject matter experts and offer platforms for sharing research findings. While the list of future research content domains is lengthy, we focus on a few topics that can be richly explored using the TIU approach.

We find it curious that a dominant view exists in marketing that "best-practice marketing" entails segmenting markets, selecting target segments, developing differentiating value propositions, and then activating with a marketing mix. Any or all of these basic steps could be challenged using a TIU approach. For example, under what conditions does segmentation still matter, and when is segmentation inappropriate? When do differentiated value propositions decrease, rather than increase, sales? And, thinking more broadly about the function, when should marketing "not have a seat" at the table in business unit strategy discussions?

The litmus test for any high performing chief executive officer, general manager, or brand manager is year over year profitable organic growth. The problem in our discipline is that we often approach growth as a marketing issue. However, from a firm perspective, the issue is how to integrate all back office and commercial functions to drive organic growth. Marketing is only one piece of this puzzle. When should marketing play a prominent (or less prominent) role in shaping the growth strategy? When is it appropriate to have "chief growth officers" lead these growth efforts? What role should marketing assume when a firm decides to hire chief growth officers—and not chief marketing officers?

This topic is front and center for most Fortune 500 firms, yet little theory exists to guide firm actions in structuring market communications, collecting consumer intelligence (e.g., traditional research methods plus digital footprints), or building customer-facing digital platforms (e.g., General Electric's recent unsuccessful attempt to build a client-facing platform for the industrial internet). Research in this domain could also closely examine the implications of digital transformation for the marketing organization within a firm. For example, whereas social media is largely viewed as an avenue for advertising and promotions, several firms are actively using social media channels for customer service, direct sales, and market research (see, e.g., efforts of KLM, the Snickers "Hungerithm" campaign).

Digital transformation of industries and markets requires executives to rethink next-generation marketing resources, capabilities, and skills their companies need to secure and grow to engage with customers in new and meaningful ways in the digital age. For example, companies today increasingly focus on rolling out new subscription-based business models. In line with this fundamental trend, a growing number of firms invest in new organizational functions, such as customer success management; they hire new staff across all hierarchical levels, from vice presidents of customer success to customer success associates. Clearly, key decision makers add new customer-facing roles and responsibilities to complement others in existing areas, such as customer experience management or key account management. What are executives' mental models underlying such decisions? Which TIUs guide managers in growing these novel marketing competencies? Research in TIU is well positioned to shed new light on this growing managerial practice.

With the 2018 emergence of General Data Protection Regulation standards in Europe, the California Consumer Privacy Act becoming law in January 2020, and current debates in Congress on the possibility of a National Commission on Public Privacy, we are witnessing an acceleration in the debate and implementation of privacy policies. The aim is to protect consumers at multiple levels—by including access to personal data (e.g., health records finance), limiting hacking, and, more generally, maintaining personal privacy. A TIU approach would be particularly useful in assessing the trade-offs that consumers are willing to make regarding the balance of sharing versus protecting their personal information. This is important wherever paradox arises, such when consumers insist on greater protection of personal data while enjoying the benefits of more personally relevant information and firm offerings resulting from firms mining their personal data. A TIU approach can be valuable in surfacing moderators that help consumers resolve such paradoxes.

In the United States, a particularly contentious debate is unfolding regarding single-payer systems, the role of government in delivering health care solutions, and the overall cost of health care. While these are large, complex issues, behind the scenes there is a sense that there are two diametrically opposed worldviews that "set context" for the debates. One of the authors of this article has been involved in a TIU project aiming to better understand how Democrats and Republicans view health care disparities to overcome political gridlock. The overall objective was to understand the fundamental frames both groups used to understand health disparities and help develop a campaign that would push the issue forward without alienating either group. It was found that, contrary to public expressions, there were important commonalities as well as differences between the two parties that served as a shared foundation for discussing their differences.

A second health care topic is connected health care. Ensuring that patients take their medicine as prescribed and achieving compliance is both a societal goal and a company goal, but what about consumers' position? Increasingly the topic of connected health becomes intertwined with privacy concerns. Many firms now remotely monitor patient compliance through medical devices (e.g., sleep apnea machines with embedded chips) and, as a result, the patient, physician, channel intermediaries, and insurance firms all have access to patient data. The overall system improvements—reimbursement based on actual compliance, better patient flow management in doctor's office, and reduced labor costs for the channel—are all very positive. However, we do not have a deep understanding of the patients' positive and negative views on connected health care.

Increasingly governments are more involved in the day-to-day affairs of for-profit and nonprofit organizations. Despite the important role that regulation plays in improving the common good (e.g., pollution controls, environmental policies, land protection, water management), there are clear reasons for for-profit firms to oppose these regulations and/or actively lobby against them. These could be for economic reasons (e.g., adverse influence on their profitability) and/or for constituency reasons (e.g., a firm is based in a region that highly depends on that particular industry sector). As noted previously, TIU is particularly useful in situations where stakeholder views may differ—or even collide.

All research methods, including TIU, merit continual improvement. Each method has strengths and weaknesses in which further inquiry can refine or enhance strengths and diminish weaknesses. Next, we discuss four areas for future research on the TIU itself.

What topic and population factors influence desired sample size? When is redundancy in constructs and construct pairing most likely to occur, suggesting that further interviews may not be productive? Rules of thumb vary between 15 and 25 participants, but more systematic clarity is needed. This is critical because travel budgets, transcription costs, and researcher time are typically scarce resources, especially when multiple populations are involved (as is the case with cross-cultural research).

A special value of TIU is its ability to directly elicit the causal mechanisms—the "hows" and "whys" supporting particular construct pairings—present among theory holders. These, of course, are critical to any theory-building enterprise. More R&D is needed to document productive and unproductive elicitation techniques for particular populations and circumstances. For instance, children often have well-developed TIUs, but eliciting them is a special challenge requiring more novel probing and elicitation processes. Separately, some probing techniques may work best in face-to-face interviews but less well for those conducted online. These are all situations requiring more study.

Some topics are inherently more challenging than others for respondents to address. This is especially the case when a topic concerns socially embarrassing issues (e.g., personal hygiene) or involves considerable implicit thinking and tacit knowledge (e.g., knowledge that may not have been given much prior explicit thought by the participant). Such taken-for-granted experiences are circumstances where TIU is especially valuable. Research is needed to identify alternative ways of using TIU interview techniques for such instances.

The TIU approach is ideally suited to surface interesting, novel theories and concepts that can advance both marketing practice and scholarship. As such, the overall objective of this article is to inspire and provide guidance on the development of knowledge based on the TIU approach. A key message of this article is that while the TIU approach requires skill, tradecraft, and practice, it has resulted in multiple breakthrough, award-winning research articles (e.g., see Table 2). These articles represent important organic marketing theories that have paved the way for long-lasting research streams that continue to inspire scholarly research today.

While impact may be a sufficient motivation, there are two additional benefits of pursuing this approach. First, researchers using this approach often find that gleaning new insights this way is a special variant of fun. The fun involves the excitement of discovering something novel as well as getting closer to the marketing phenomena. Giving time to executives, consumers, and policy makers to explore their own thinking is also rewarding. Furthermore, having one's own ideas challenged by interviewees can shake a scholar out of the routine of reviewing literature written by other academics ([32]). Second, TIU research not only represents a great vehicle for bringing relevance to the classroom but also provides a platform for sharing real-time stories and challenges that are unfolding in practice. Moreover, in our experience, managers taking part in the research are often excited about the prospect of becoming long-term partners in the research and education process.

In conclusion, if the field of marketing is to continue to have relevance for the practice of marketing, we must develop ideas, concepts, and theories whose central focus is the study of marketing in its natural environment. Within this environment, managers, consumers, and policy makers are a wonderful source of new ideas, unconventional thinking, and ways of working that can fundamentally reshape our current thinking and theories. One can "go it alone" by reading marketing literature and coming up with ideas, or one can capitalize on the knowledge of managers, consumers, and public policy makers who are dealing with significant, underresearched challenges every day. We hope our team experience captured in this article will facilitate your focus on the latter!

Supplemental Material, jm.19.0111-File003 - A Theories-in-Use Approach to Building Marketing Theory

Supplemental Material, jm.19.0111-File003 for A Theories-in-Use Approach to Building Marketing Theory by Valarie A. Zeithaml, Bernard J. Jaworski, Ajay K. Kohli, Kapil R. Tuli, Wolfgang Ulaga and Gerald Zaltman in Journal of Marketing

1 Authors' NoteAuthor ordering is alphabetical except for the first author, who also served as the team leader.

2 EditorsRobert W. Palmatier and Christine Moorman

3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

4 Online supplement: https://doi.org/10.1177/0022242919888477

5 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.

6 1We thank John Sherry for his helpful comments in this section.

7 2If the core construct to be investigated is not yet clear, participant conversations may suggest a different construct that the researcher may ultimately decide is more fruitful to pursue than the one they started with.

8 3We should note here that there is nothing wrong per se with relying on a particular participant's TIU. However, our experience is that participants rarely hold theories that are formed well enough to be suitable for publication in academic journals.

9 4This guide should be provided in the published article.

5The quotes in the current research are approximate, and some examples are stylized.

6For a discussion of two specialized techniques for theory construction (the Kelly Repertory Grid and the Zaltman Metaphor Elicitation Technique), see the Web Appendix.

7Nonetheless, it may be useful to briefly note antecedents, consequences, or moderators that may be obvious/intuitive but important such that a reader has a more complete understanding of the phenomenon of interest.

8This differs from the meaning of credibility in classical interpretive research, where it refers to the consistency between the account provided by a researcher and the data. This difference arises because in TIU research the focus is on if-then propositions and their credibility (more than that of a researcher's account of participants' input).

9This differs from the meaning of dependability in classical interpretive research, where it refers to the stability or consistency of participant reports about a phenomenon across time. This difference arises because, in TIU research, the focus is on if-then propositions and their dependability (rather than that of each participant's report).

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By Valarie A. Zeithaml; Bernard J. Jaworski; Ajay K. Kohli; Kapil R. Tuli; Wolfgang Ulaga and Gerald Zaltman

Reported by Author; Author; Author; Author; Author; Author

A Theory of Customer Valuation: Concepts, Metrics, Strategy, and Implementation 

Customer value refers to the economic value of the customer’s relationship with the firm. This study approaches the topic of customer value for measuring, managing, and maximizing customer contributions by proposing a customer valuation theory (CVT) based on economic principles that conceptualizes the generation of value from customers to firms. The author reviews the established economic theories for valuing investor assets (e.g., stocks) and draws a comparison to valuing customer contributions. Furthermore, the author recognizes the differences in the guiding principles between valuing stocks and valuing customers in proposing CVT. Using CVT, the author discusses the concept of customer lifetime value (CLV) as the metric that can provide a reliable, forward-looking estimate of direct customer value. In addition, economic models to estimate CLV, ways to manage CLV using portfolio management principles, and strategies to maximize CLV are discussed in detail. The author extends the customer value concept by discussing ways that a customer can add value to the firm indirectly through incentivized referrals, social media influence, and feedback. Finally, the benefits of CVT to multiple constituencies are offered.

Any sustainable business first creates value for its customers through firm offerings1 and, in the process, derives value from its customers in the form of profit.2 This duality of roles performed by firms and customers to derive and deliver value best summarizes the firm–customer relationship from a value standpoint. However, this value is distributed heterogeneously across customers. Because it is the firms/decision makers who allocate resources to markets, customers, and products, the challenge in this context for firms is to dynamically align resources spent on customers and products to simultaneously generate both value to customers and value from customers. In addition, the volatility and vulnerability in customer cash flows differentially affects overall firm profitability. These changes can be due to both customer actions and firm actions. For instance, especially in business markets, leadership change in the customer organization influences procurement decisions that can result in changes in future cash flows. Similarly, customer life events (e.g., getting divorced, becoming empty nesters) can also affect future cash flows. Therefore, firms look for ways to better manage cash flows (Srivastava, Shervani, and Fahey 1999).

To better understand and manage the value creation and cash flow management process, this article proposes a theory to value future customer contributions: the customer valuation theory (CVT). The CVT focuses on two aspects of customer financial contributions: their nature (i.e., direct and indirect) and their scope (i.e., breadth and depth). In doing so, the CVT informs firms about ( 1) the conceptualization of value generation from customers and ( 2) the ways and means available to generate and maximize value from customers. In this regard, the power of the customer lifetime value (CLV) metric to accurately value a customer’s future contributions is established. By applying the CLV metric, this study demonstrates how firms can use CVT to ( 1) value customer assets, ( 2) manage customer portfolios, and ( 3) nurture profitable customers. The robustness of the CVT is also highlighted through its successful implementation across various types of markets (consumer and business), scenarios (contractual and noncontractual business settings), contexts (domestic and global), and industries (e.g., insurance, airline, retail). Furthermore, the widespread positive impact of implementing CVT on multiple constituents of the market such as the firms, the customers, the employees, the society, and the environment is also identified.

What is an asset? Despite the conflicting viewpoints on the role and constituents of an asset or firm resource (Mahoney and Pandian 1992), an asset can be broadly defined as any physical, organizational, or human attribute that enables the firm to generate and implement strategies that improve its efficiency and effectiveness in the marketplace (Barney 1991). Given the prominence of assets in deploying firm strategies and gaining competitive advantage, the next question is, What makes them valuable? Here too, scholars from diverse fields such as finance, industrial organization, management, and economics have presented several approaches to decode an asset’s value. With specific reference to marketing, academics and practitioners consider customers as assets and have instated strategies that manage and nurture customers rather than use them only for specific marketing actions. In this regard, studies have investigated customer asset valuation from various viewpoints such as firm valuation (Gupta, Lehmann, and Stuart 2004; Kumar and Shah 2009), customer management (Berger et al. 2002), and financial performance (Hogan et al. 2002), among others. The differing approaches to asset valuation notwithstanding, the true value of an asset is most often observed in its interactions in the external marketplace. This leads to the next question: How are assets valued? To better understand the approach to value assets, consider two scenarios—an investor investing in stocks, and a firm investing in its customers.

In the case of the investor (which applies to both individual and institutional investors), the valuation of stocks forms a crucial component. Drawing on the valuation, the investor typically performs three routine actions: (s)he ( 1) selects and invests in stocks that show potential for growth, ( 2) constructs a portfolio of stocks and bonds, and ( 3) constantly rebalances the portfolio to ensure maximum future gains. An important feature common to all these actions is the element of risk. This is because risk affects the volatility and vulnerability of cash flows, which, in turn, affect the stock value and ultimately reflect in the overall firm value.3 As a result, investors aiming to avoid risk ideally should ( 1) invest in stocks that indicate a steady stream of cash flow, ( 2) construct a portfolio of similar stocks so that their overall future value is secured and maximized, and ( 3) constantly evaluate the earnings potential of the stocks in their portfolio and reconfigure the portfolio by selling risky stocks and buying robust stocks.

To assist investors in the stock valuation process, several approaches, such as the discounted cash flow models, capital asset pricing models, and the arbitrage pricing theory, among others, are available. The resulting stock valuation would then inform investors about the configuration of the portfolio using approaches such as the modern portfolio theory (Elton et al. 2014).

In the case of the firm investing in its customers, the valuation of customers also forms a crucial component. If the principles of the stock valuation approach were applied to manage investments in customers, firms ideally should perform the following three actions: ( 1) identify and invest in the “right” customers, ( 2) form a customer portfolio (or customer base) consisting of favorable customers, and ( 3) constantly reevaluate the portfolio to ensure that the firm is maximizing its future gains. In reality, performing each of these three actions is not always possible because of certain challenges.

The comparison between the case of the investor and the firm shows the similarity in how they manage their respective assets (stocks vs. customers). So, can the principles that guide the valuation of stocks be applied in valuing customers? To answer this question, the challenges of valuing customers must first be understood.

First, firms need a reliable method to identify and invest in the “right” customers. While it may seem straightforward to say that the “right” customers are the ones who exhibit the highest value potential, the specifics may not be so apparent. Traditionally, value measures have focused on repeat purchases, acquisition cost, retention cost, tenure, and share of wallet, among others. Thus, a valuation approach that can accurately capture these intricacies for firms to identify the most valuable customers is essential.

Second, configuring a portfolio of the most valuable customers is easier said than done. The customer characteristics (e.g., consumption pattern, lifestyle habits) that determine their value potential have been found to change over time (Kumar 2013). As a result, periodic evaluation of customer value measures (e.g., profitability) is a necessary and reliable method to help managers in the vital job of keeping track of changing customer characteristics. Furthermore, regulated monopolies such as telephone and municipality services are often required to cross-subsidize one group of customers with another (e.g., rural and urban customers). In such cases, firms may not be able to build the ideal portfolio of customers.

Third, rebalancing of customers is not a feasible strategy. Although cases have been reported in which companies have fired customers because profitability concerns (Reardon 2007), this is not a common practice. For instance, many banks are unable to “fire” the unprofitable customers, especially if they are from lower socioeconomic groups or minority groups. In addition, churn is a challenge that firms constantly face. While a firm may want to have profitable customers, holding on to those customers typically poses challenges for the firm. Therefore, firms need a reliable way to discern how to manage unprofitable customers as well as ways to nurture profitable customers.

Therefore, in this study, I present the case for a valuation approach that is specifically designed to value customer assets. In doing so, I demonstrate why the stock valuation approach is not readily applicable to valuing customer assets and show how customer assets are uniquely positioned to provide value to the firm.

TABLE: TABLE 1 Summary of Select CLV Modeling Approaches

TABLE: TABLE 1 Summary of Select CLV Modeling Approaches

The theoretical underpinnings of how an investor values stocks and a firm values its customers can be understood through the following questions:

• How do firms view customer assets?

• Why are financial theories inappropriate for valuing customers?

• How does customer valuation work?

How Do Firms View Customer Assets?

Research on customer asset management dates to models that explored how consumers make purchase decisions (Howard and Sheth 1969). The research insights generated since then have led to the consideration of customers as integral to organizations (Gupta and Lehmann 2005; Shah et al. 2006). In this regard, studies have considered customers to be intangible assets of a firm (Hunt and Morgan 1995; Srivastava, Shervani, and Fahey 1998) and have proposed approaches to valuing and managing their contributions to the firm (Bolton, Lemon, and Verhoef 2004; Reinartz and Kumar 2000).

Studies have also focused on applying customer value to enhance firm performance from various perspectives such as the role of customer acquisition strategies (Lewis 2006), customer retention strategies (Reinartz, Thomas, and Kumar 2005), customer loyalty (Reinartz and Kumar 2002), customer satisfaction (Anderson and Mittal 2000), resource allocation (Petersen and Kumar 2015), and customer metrics (Petersen et al. 2009; Srinivasan and Hanssens 2009), among others. Such attempts continue to shape profitable customer management (based on future customer profitability) for both contractual and noncontractual business settings.

Research on customer value has also explored the volatility and uncertainty of future revenue contributions. In this regard, studies have identified that certain behavioral drivers on the part of customers (e.g., level of purchases, product return behavior) determine the level and volatility of cash flows (Kumar, Shah, and Venkatesan 2006; Reinartz and Kumar 2003). Therefore, the management of behavioral drivers is critical in valuing customers.

Why Are Financial Theories Inappropriate for Valuing Customers?

Prior studies have investigated the application of financial theories to marketing decisions. For instance, Cardozo and Smith (1983) proposed an approach for making product portfolio decisions by applying the financial portfolio theory. However, Devinney, Stewart, and Shocker (1985) highlighted critical differences regarding applying a financial theory in a product decision setting. Similarly, Tarasi et al. (2011) demonstrated the application of financial portfolio theory for making customer portfolio decisions, which subsequently attracted critical review in the literature (Billett 2011; Selnes 2011). Other efforts that have incorporated concepts from financial theories into marketing applications include the introduction of customer beta that measures the riskiness of customers (Dhar and Glazer 2003), a customer relationship scorecard based on customers’ risk–return characteristics (Ryals 2003), and the management of customer segments using portfolio theory (Bolton and Tarasi 2015; Buhl and Heinrich 2008; Groening et al. 2014). In light of these efforts to apply financial theories for valuing customers, some principal differences between finance and marketing must be noted. Figure 1 illustrates these differences as well as how they ultimately affect firm value. They are as follows:

• It is possible to invest money into stocks and achieve a higher amount in return. However, this is not possible in the case of customers. Firms know that beyond a certain point, investing more money in their customers will yield a lower rate of return. In other words, whereas the investment-to-earnings relationship can be linear in the case of stocks, it is nonlinear in the case of customers.

• Investors can be fairly certain about a stock’s “life expectancy” and the survival of that firm. However, firms that invest in customers can make no such conclusion about their customers. In other words, investors have relatively more information about how long the stocks in which they have invested will remain in trading, compared with firms’ knowledge of how long their customers will remain their customers.

• In the case of widely held stocks (i.e., those that are not closely controlled by investors and fund owners), if a stock begins to perform poorly, the investor has the option of quickly divesting that stock. After divesting, the investor has the option of either buying another stock or just holding on to the cash. However, firms typically do not have prior information about how much value a customer is going to bring in. Furthermore, changes in customer lifestyles may make customers less profitable or may even cause losses. In such a case, the firm typically does not “divest” of such loss-making customers but has to find ways to manage them appropriately. This is also true in certain business settings (e.g., oil industry) in which it is difficult for suppliers to exit a customer relationship. In some cases, such situations can lead to speculative business practices such as stockpiling. In other words, the value and volume of investments in stocks is easily scalable, compared with the investments in customers.

• Investors routinely buy and sell stocks that will increase the value to the portfolio and/or minimize the risk of losing value.

However, firms that invest in customers do not have the luxury of hiring and firing customers. As a result, a low-value customer is still likely to be part of a firm’s customer portfolio, and firms will have to find ways to manage these customers in such a manner that they do not lose firm value. In other words, rebalancing a stock portfolio is easier than rebalancing a customer portfolio.

• Although investors value their investments in widely held stocks in line with the projected cash flows of the respective stocks,4 firms that have invested in their customers assess the value of their customers on the basis of customer contributions to firm revenue, which determines the stock price and, ultimately, the firm’s value. Recognizing this chain of impact is important in developing a metric that can effectively track the firm’s value creation. In other words, whereas the impact of investing in customers can be observed on the value of stocks (and, ultimately, on firm value), investing in widely held stocks has a limited observable impact on the value of customers.

• Using financial theories, it is possible for investors to identify the types of risks to which stocks are exposed (e.g., given the economic cycles) and recognize the ones that can be diversified from the ones that cannot. However, firms that have invested in their customers cannot readily identify the risks from customer contributions, or their impact on profitability. In other words, it is relatively easier to identify (and therefore manage) risks arising from investments in stocks compared with investments in customers.

• Investor sentiments play an important role in investment decisions (Weber and Johnson 2009). Stock market operations involve investor sentiments regarding a firm’s future performance expectations, which, in turn, determine the level of attractiveness of that firm in the industry. In contrast, the influence of investor sentiment is not a significant force when valuing a customer’s direct contribution. However, for customers who are based in politically unstable regions, the valuation is different more for sentimental reasons than for economic reasons. In other words, the importance of investor sentiment is higher in the valuation of stocks than in the valuation of customers.

• Speculation also plays an important role in investment decisions. For instance, speculation is based on a rational betting decision that is known to stabilize asset prices (Friedman 1953) and is sometimes based on insider trading (Kyle 1985). Furthermore, it is known that if the investment actions of rational speculators trigger the buying of securities when prices rise and selling when prices fall, then an increase in the number of forward-looking speculators can increase volatility about the asset fundamentals (De Long et al. 1990). In contrast, speculation does not play a major role when assessing the value of a customer. In other words, the importance of speculation is higher in the valuation of stocks than in the valuation of customers.

• Drawing on known future discounted cash flows, an investor typically decides on his or her choice of investment. That is, the options are limited to either investment or divestment. As a result, investors generally cannot influence the future cash flow patterns of a firm to change the course of their own actions. In other words, financial theories offer a passive approach to managing investments, whereas customer management requires an active management approach.

• The volatility and vulnerability of stocks make it difficult for investors to predict stock returns in the short run. Short-term returns are difficult to predict because of their random walk feature (Jensen 1978; Malkiel 1995). In addition, Kumar, Ramaswami, and Srivastava (2000) observe that daily returns are sensitive to random disturbances in the market. To predict stock movements, they offset the effect of random disturbances by considering a longer time period such as a month. Using the principles advanced by the capital asset pricing model and the arbitrage pricing theory, the study developed a multistage model to study variation in stock returns. The study found that the addition of significant factors other than the market factors (i.e., cost and supply of money) increases the level of risk, which results in a decrease in the price of the firm. In this regard, appropriately designed marketing actions directed at the environmental uncertainties (i.e., macroeconomic factors) can lessen the impact on the firm’s cash flows, because predictions of customer value are accurate in the short run. Therefore, estimating the value of customers and pairing this value with appropriately designed marketing strategies can place firms on the path to increased financial returns. The level of predictive accuracy of customer value, however, declines only in the long run. In other words, a more accurate prediction of stock returns is possible only in the long run, compared with the prediction of customer value, which is more accurate in the short run.

In addition to the financial theories, knowledge from behavioral finance is also relevant here. Behavioral finance literature has argued that some financial phenomena can reasonably be understood using models in which some agents are not fully rational (Barberis and Thaler 2003). With specific reference to investor behavior, behavioral finance has explained how certain investor groups behave, the types of portfolios investors hold, and investors’ trading patterns over time. Barberis and Thaler (2003) trace investor behaviors such as having insufficient diversification of portfolios (Baxter and Jermann 1997), opting for simpler diversification strategies (Benartzi and Thaler 2001), having high trading volumes (Barber and Odean 2000), holding on to stocks even if they are decreasing in value (Odean 1998), and considering “attention-getting” stocks for their purchase decisions (Barber and Odean 1999) to demonstrate that investors are not always rational in their investment decisions. This stream of literature has established that investors’ attitudes and behaviors are of vital importance. In light of the aforementioned differences between customers and stocks, I develop the CVT as a robust theory for valuing customer assets by adopting a different approach than that used to value stocks.

How Does Customer Valuation Work?

From the previous discussion of the determinants of customer assets, one of the approaches to value customers in a general form is presented next:

CFPi = f (Transaction behaviori, Marketing costi, Demographic=firmographic variablesi, Economic and environmental factors ), where CFPi refers to customer future profitability of customer i.

Simply put, the future profitability of the customers depends on their past and current transaction behaviors, the marketing efforts of the firm, the identity and profile of the customers (i.e., demographic variables), and the environment in which these customers exist (i.e., economic and environmental factors). Furthermore, when modeling CFP, statistical issues such as heterogeneity, endogeneity, and simultaneity are factored in the estimation of such models. Once the CFP is modeled, business intelligence software systems can be used to score CFP, update customer information, and rescore CFP on a periodic basis. Technology can also be incorporated to target customers on a real-time basis through relevant messages in an effort to increase future cash flows. Drawing on this valuation approach, I advance the following testable propositions.

Transaction behavior. Also known as exchange characteristics, the transaction behavior broadly includes all the past and current transaction variables that affect and influence the customer–firm relationship. The commitment-trust theory (Morgan and Hunt 1994) proposes that firms try to establish positive relationships with customers by developing commitment and trust with them. While a customer’s positive affect influences his or her commitment to the firm, research has also uncovered other dimensions of customer commitment. For instance, Allen and Meyer (1990) proposed a three-component model of commitment (affective, calculative, and normative). Recently, Keiningham et al. (2015) proposed customer commitment as a five-dimension construct (affective, normative, economic, forced, and habitual). Beyond repeat purchases, purchase habits have been found to play an important role in determining customer transaction behavior (Ascarza, Iyengar, and Schleicher 2016; Duhigg 2013). Specifically, research has reported that customer habits influence the future volatility and vulnerability of cash flows (Shah et al. 2017) and firm performance (Shah, Kumar, and Kim 2014). Furthermore, the importance of primary market research in understanding customer behavior patterns has also been highlighted, especially in light of the abundance of behavioral information made possible by big data (Knowledge@ Wharton 2014). As a result, frequent customer–firm interactions should increase customer trust and commitment in the firm at a faster rate, provided that the interactions are satisfactory. Therefore,

P1: Customer transaction activities significantly influence customer future profitability.

This proposition has been tested across various industries and markets (business to business [B2B] and business to customer [B2C]), and research has found that customer future profitability is positively influenced by a host of variables, including ( 1) prior customer spending level (Reinartz and Kumar 2000), ( 2) customer cross-buying behavior (Kumar, George, and Pancras 2008; Venkatesan and Kumar 2004), ( 3) the intensity of customer purchases within a product category (Reinartz and Kumar 2003), and ( 4) members of rewards programs with the firm (Kumar, Shah, and Venkatesan 2006). In addition, studies have also found the average interpurchase time and the number of product returns to have a significantly positive impact on customer future profitability, up to a certain threshold (Reinartz and Kumar 2003).

Marketing cost. Marketing cost can include past, current, and future promotional costs (toward customer acquisition, retention, and win-back); technology upgrades; service improvements; employee management; and quality control. The importance of effective management of customer assets to enhance firm profitability (Bolton, Lemon, and Verhoef 2004) has directed research attention toward understanding the impact of marketing expenditures on customer value and actively using marketing communication actions (i.e., customer contact channels) to maximize customer value. Prior research has established that well-timed communication efforts (Kumar, Venkatesan, and Reinartz 2008) and well-managed content (Kim and Kumar 2018) between firms and customers reduces the propensity of a customer to quit a relationship (Morgan and Hunt 1994). However, too much communication has also been found to be detrimental to the relationship (Fournier, Dobscha, and Mick 1998), thereby indicating the presence of an optimal communication level.

P2: Marketing cost nonlinearly influences customer future profitability.

Studies have revealed an inverted U-shaped relationship between marketing contacts (involving rich modes [e.g., sales personnel contact] and standardized modes [e.g., telephone, direct mail]) and customer behavior (Reinartz, Thomas, and Kumar 2005; Venkatesan, Kumar, and Bohling 2007). Furthermore, in a permission-based marketing context, a firm’s marketing contact policy influences both the length of time a customer stays in an email program and the average amount a customer spends on a transaction while (s)he is subscribed to the email program; too much marketing may not only make customers less likely to opt in but also cause them to opt out more quickly (Kumar, Zhang, and Luo 2014).

Demographic/firmographic variables. Demographic/firmographic variables refer to the distinguishing characteristics of the customer (end user or business customer). In the case of a business customer, the firmographic variables include the type of industry, the age and size of the firm, the level of annual revenue, and the location of the business. In the case of an end user, the demographic variables include age, gender, income, and the physical location of the customers. The demographic/ firmographic variables can aid firms in characterizing attractive segments into identifiable and measurable groups of customers (Zeithaml 2000). In the case of end users, the heterogeneity in profit contributions can be better understood through a customer’s demographic and psychographic variables (Chintagunta, Jain, and Vilcassim 1991); demographic variables affect customer store choice (Craig, Ghosh, and McLafferty 1984), shopping channel choice (Inman, Shankar, and Ferraro 2004), profitable lifetime duration (Reinartz and Kumar 2003), and migration of shopping choice (Thomas and Sullivan 2005), to name a few. As a result, classifying customers on the basis of their distinguishing characteristics can help firms in their customer segmentation and customer relationship management efforts. Therefore,

P3: Demographic/firmographic variables significantly influence customer future profitability.

The testing of this proposition has revealed that demographic variables such as household income, population density of the neighborhood, gender, age of the head of the household, marital status, education level, and so forth significantly affect future customer profitability (Kumar, George, and Pancras 2008; Kumar, Shah, and Venkatesan 2006). Furthermore, research has also established that firmographic variables such as the size of the firm and the industry category of the business customers significantly explained the variation in contribution margin for the focal firm (Venkatesan and Kumar 2004). Specifically, Venkatesan and Kumar (2004) found that the focal firm’s business customers in the financial services, technology, consumer packaged goods, and government industry categories provided, on average, a higher contribution margin than firms in other industry categories.

Economic and environmental factors. Economic factors such as gross domestic product (GDP) per capita help determine the consumption pattern of a country. It has been established that consumers’ response to macroeconomic factors is a function of not just their ability to buy (as measured by current and expected future income), but also their willingness to buy (Katona 1975). This underlying theory explains how various macroeconomic conditions affect price changes (Gordon, Avi, and Yang 2013), changes in consumers’ frame of mind (Chhaochharia, Korniotis, and Kumar 2011), and overall household utility (Kamakura and Du 2012), to name a few. Furthermore, research has also shown that changes in consumers’ economic constraints have varying effects on their profit-contributing potential. For instance, Sunder, Kumar, and Zhao (2016) demonstrated that high-CLV customers are least affected by changes in their budgetary constraints when compared with low-CLV customers. Therefore, if a country has a high GDP and high purchasing power, its consumers will have more disposable income and spend more.

P4: Economic and environmental factors significantly influence a customer’s future profitability.

Studies that have tested this proposition have found that GDP per capita, the country’s economic well-being (how customers feel about and experience their daily lives), cultural characteristics, and the employment rate significantly influence future customer profitability (Kumar and Pansari 2016b; Kumar et al. 2014; Umashankar, Bhagwat, and Kumar 2016).

In line with the customer valuation approach discussed previously, the CVT can be defined as a mechanism to measure the future value of each customer on the basis of ( 1) the customer’s direct economic value contribution, ( 2) the depth of the direct economic value contribution, and ( 3) the breadth of the indirect economic value contribution by accounting for volatility and vulnerability of customer cash flows. The key components of this definition include:

• Direct economic value contribution: This refers to the economic value of the customer relationship to the firm, expressed as a contribution margin or net profit. A firm can both measure and optimize its marketing efforts by incorporating customer value at the core of its decision-making process. When implemented at firms, it aids in ( 1) computing a customer’s future profitability, ( 2) arriving at a good measure of customer value, ( 3) optimally allocating marketing resources to maximize customer value, and ( 4) identifying ways to maximize the return on marketing investments.

• Depth of direct economic value contribution: This refers to the intensity and inclusiveness of customers’ direct value contributions to the firm through their own purchases that have produced significant financial results for the implementing firms. Examples of such instances include the acquisition and retention of profitable customers on the basis of their future value potential, customer purchase potential across multiple channels of buying, and the possibility of customers to buy across multiple product categories.

• Breadth of the indirect economic value contribution: This refers to customers’ indirect value contributions to the firm through their referral behavior, their online influence on prospects’ and other customers’ purchases, and their feedback on the firm offerings. These indirect measures also contribute significantly to a firm’s cash flow. This indirect contribution can also be extended to accommodate other contexts such as salesperson productivity, donations (in the case of nonprofit firms), and business references.

Because CVT enables managers to actively manage customer relationships on the basis of future customer contributions through specialized customer strategies, it creates a positive impact on firm performance. Specifically, the implementation of the CVT can help firms improve their marketing productivity and realize higher firm value through ( 1) valuing customers as assets, ( 2) managing a portfolio of customers, and ( 3) nurturing profitable customers. Therefore, it is critical to understand the nature of the linkage between CVT and firm value. Figure 2 provides an overview of how the components of CVT function in driving firm value.

In summary, the proposed CVT is relevant for the following reasons:

• Unlike prior marketing applications of the financial portfolio theory, this theory focuses on customer management at the individual customer level (on the basis of his or her profitability) instead of the customer segment level. Although customers are grouped in line with their profitability (i.e., high-, medium-, and low-profit segments), the subsequent strategies that are developed are always deployed at an individual customer level because this is more effective. Such an approach is different from how investors handle financial assets. The CVT implicitly accounts for customer risks (i.e., volatility and vulnerability in cash flows) when modeling future customer profitability. In doing so, the model focuses on how the risks ultimately affect customer profitability (and thereby, overall firm profitability) and treats it accordingly. For this reason, the CVT does not explicitly provide a beta value or any indicator for the risk-free return equivalent in the case of investments. Furthermore, the CVT enables managers to identify and manage the diversifiable risks through tailored offerings by focusing on the drivers of customer value.

• Because the CVT focuses on customer profitability, the variation in associated customer costs at the individual level are accounted for.

• The CVT is highly active in terms of generating actionable firm strategies. By dynamically managing the volatility and vulnerability in cash flows, this theory enables managers to actively monitor customer relationships over time and undertake necessary remedial measures. In comparison, the financial theories largely suggest that investors invest (or divest) at any point in time as determined by the discounted cash flow analyses. As a result, the financial theories remain passive by not providing adequate directions to investors such that they can influence future changes.

• Because future costs drive future margins, the CVT-based strategies advise managers to better manage their acquisition and retention of profitable customers; by doing so, firms can actively refine and manage the customer valuation approach. However, no such option is available with investing in stocks because stocks yield fixed returns, and the investor cannot influence or manage the return on a stock.

• The CVT recognizes the investment-to-earnings relationship, in the case of customers, to be nonlinear (unlike whenn valuing stocks), and it uses an appropriate customer valuation approach. This is subsequently reflected in customer strategies that can be developed.

The next component of the CVT delves into the concepts behind customer valuation and the related financial benefits they hold for firms. To contextualize the practice of customer valuation to the proposed theory, it is essential to review prior literature on this topic. In recent years, the idea of treating customers as assets of a firm has emerged as the most popular and efficient way of doing business (Hunt and Morgan 1995). This entails identifying future customer profitability and designing marketing guidelines that will advise managers on profitable customer management. Traditionally, firms have used several metrics to value customers (Kumar and Reinartz 2012; Petersen et al. 2009). The guidance from these metrics has driven decisions pertaining to the allocation of marketing resources.

When considering a customer’s value contribution to the firm, a crucial part is his or her contribution in the future periods. It is this future component that is of immense interest to academics and practitioners. The concept of future value contribution has been conceptualized in the form of the CLV metric, which refers to the present value of future profits generated from a customer over his or her lifetime with the firm (Gupta and Lehmann 2005; Venkatesan and Kumar 2004).

The conceptualization of CLV was strongly influenced by the corporate finance body of knowledge, and specifically to the contribution of the present value concept by Irving Fisher (1965 [1930]) and Eugene Fama’s asset pricing theory (Fama and Miller 1972). Applying this knowledge to customer asset management, it is evident that customers pose risks in terms of generating returns for the firm in the future. However, the impact of these risks on customer profitability is not uniform across all customers. In this regard, the CLV approach identifies opportunities to contain the variation in returns (also known as cash flow volatility) and, thus, the total risk of changes in the value of the firm (measured by future returns) (Shah et al. 2017). This is possible by understanding the drivers of customer profitability and their impact on CLV.

While the inspiration from other sciences such as economics and finance is apparent, the adaptation and inclusion into the customer asset valuation would not have been possible without the development of new methodologies specific to the marketing milieu. Specifically, the conceptualization of the CLV metric and the development of substantive methodologies served as a stepping stone for managing profitability by selecting the right customers for targeting and determining the allocation of resources for customer acquisition, retention, and growth. Furthermore, this important contribution also demonstrated that the CLV framework can help firms manage risk through appropriate actions directed at the individual customers.

Direct Economic Value Contribution

While the valuation of assets/projects is typically done at the aggregate level, the CLV metric enables firms to capture the direct value contribution of customers at both the aggregate and the individual levels. At the aggregate level, the average lifetime value of a customer is derived from the lifetime value of a cohort, segment, or even a firm. Here, the estimation of CLV can be accomplished by identifying and measuring the factors that drive CLV. At the individual level, the CLV is calculated as the sum of cumulated cash flows—discounted using the weighted average cost of capital—of a customer over his or her entire lifetime with the company (Kumar 2008). It is a function of the predicted contribution margin, the propensity for a customer to continue in the relationship, and the marketing resources allocated to the customer. It is important to note that weighted average cost of capital is one of the measures that represent the discount factor used to compute CLV, among other options (e.g., T-bills rate). In its general form, CLV can be expressed as follows (Venkatesan and Kumar 2004):

where i is the customer index, t is the time index, T refers to the number of time periods considered for estimating CLV, and d is the discount rate.5 The CVT proposed in this study is based on the individual-level CLV rather than the aggregate level.

Although a “true” CLV measure implies measuring the customer’s value over his or her lifetime, for most applications it is a three-year window.6 The reasons for this time frame are as follows: ( 1) because future cash flows are heavily discounted, a significant portion of profit can be accounted for in the first three years, and the contributions in the following years are very small; ( 2) the predictive accuracy of the models decline over a longer time frame; ( 3) changes in customer needs and life cycle are likely to change significantly beyond a three-year window;

( 4) product offerings change in response to technological advancements and customer needs; and ( 5) CLV predictions are updated on the basis of a rolling time horizon to accommodate changes in other environmental factors. However, specific industry trends do lead to some exceptions for this three-year window. For instance, automakers can expect customers to make a purchase every four to six years (we suggest using a longer window to accommodate at least a couple of purchases or using purchase intention measures to forecast future value), computer manufacturers can expect customers to make a purchase every one to two years (we suggest a four- to five-year window to account for at least two or three purchases); and insurance companies can take up to seven years to recover the acquisition costs. Exceptions aside, the aforementioned reasons advocate the use of a three-year timeframe in computing the CLV.

The extant CLV literature has covered a wide range of business conditions through the measurement approaches. This coverage has expanded the scope and application of CLV-based models for a multitude of industries and markets. Some of the later developments in modeling CLV have accommodated several modeling challenges, which has allowed for more sophisticated and precise estimation of customer value. Kumar and Reinartz (2016) provide a detailed review of select approaches that have made significant contributions in modeling CLV. Table 1 offers a summary of the model form as well as merits and shortcomings of popular modeling approaches.

Although the models discussed here are the most popular ones, there will always be improvements to the CLV model because of the nature of the availability of customer data and the business situation. In addition, the knowledge of how to implement the models is also important in determining how CLV can be managed at different firms.

Depth of Direct Economic Value Contribution

The depth of direct economic value contributions, as measured by CLV, have focused on the impact of customers’ direct profit contributions to the firm through their own purchases. Tracking and valuing these contributions have produced significant financial results for the implementing firms. In this regard, recent research has demonstrated that when firms try to understand and leverage the true power of measuring and maximizing CLV, it ultimately results in enhanced firm value (Berger et al. 2002; Kumar, Ramaswami, and Srivastava 2000; Kumar and Shah 2009). Furthermore, Kumar (2008, 2013) explored the implications and generated the “Wheel of Fortune” strategies that have enabled firms to address marketing issues with greater confidence and ensure better decision making. This set of strategies answers the following questions:

1. Customer selection: How do firms identify the “right” customers to manage? Reinartz and Kumar (2000) found that long-life customers are not necessarily profitable customers, and the authors call for the use of a forward-looking metric such as CLV to identify the “right” customers to manage.
2. Managing repeat purchases and profitability simultaneously: How can firms ensure profitability while improving customer repeat purchases? When customized actions were implemented at a B2C catalog retailer on the basis of segmenting customers on repeat purchases and profitability, Reinartz and Kumar (2002) found that loyal customers are aware of their value to the company and demand premium service, believe they deserve lower prices, and spread positive word of mouth only if they feel and act loyal.
3. Optimal allocation of resources: Which customers should the firms interact with through inexpensive channels (e.g., Internet, phone), and which customers should firms let go? When resources were reallocated on the basis of the optimal mix and frequency of communication channels, a B2B company realized 100% more revenue and 83% more profits across its four customer segments (Venkatesan and Kumar 2004).
4. Cross-buy: How can customers’ purchases be increased across multiple product categories to improve customer profitability? By encouraging customers to buy across more product categories through profitable customer management strategies, Kumar, George, and Pancras (2008) found that metrics such as revenue per order, margin per order, revenue per month, margin per month, and orders per month increased as customers shopped across multiple product categories. However, not all cross-buying is good. Shah et al. (2012) found that across five B2B and B2C firms, 10%–35% of the firms’ customers who cross-buy are unprofitable and account for a significant proportion (39%–88%) of the firms’ total loss from its customers. Therefore, discerning profitable from unprofitable cross-buying behavior is essential.
5. Next logical product: How do firms decide the timing of an offering to a customer? When done right, results have shown that firms increased their profits by an average of $1,600 per customer, representing an increase in return on investment (ROI) of 160% (Kumar, Venkatesan, and Reinartz 2006).
6. Preventing customer attrition: How do firms decide which prospect will make a better customer in the future and is therefore worthwhile to acquire? Using test and control groups, Reinartz, Thomas, and Kumar (2005) showed that acquiring and retaining the “right” customers garnered a B2C firm an incremental profit of $345,800 with an ROI close to 860%.
7. Product returns: Should the firm encourage or discourage product return behavior, and how should it manage this process? Petersen and Kumar (2009) found that the ideal level of product returns should be one that maximizes firm profits. For a B2C catalog retailer, they found that the optimal percentage of product returns that maximized profitability was 13%. Furthermore, Petersen and Kumar (2015) addressed the aspects of perceived risk and optimal resource allocation into the product returns process for a B2C catalog retailer and found that the firm was able to generate approximately $300,000 more in profits compared with the next best available resource allocation strategy.
8. Managing multichannel shoppers: What kind of sales and service resources should firms allocate to current customers to conduct future business with them? Kumar and Venkatesan (2005) identified the drivers of profitable multichannel shopping behavior and found that adding one more channel resulted in an average net gain of about 80% in profits.
9. Branding and customer profitability: Should firms invest in building brands or customers? Kumar, Luo, and Rao (2016) have shown that by understanding the link between investments in branding and CLV, firms can efficiently allocate their resources to improving customer brand value to generate maximum lifetime value. It was found that a 5% increase in the investments in branding causes the CLV to increase by over 25%.
10. Acquiring profitable customers: How should firms monitor customer activity to readjust the form and intensity of their marketing initiatives? In managing firm actions regarding customer acquisition and retention efforts, Reinartz, Thomas, and Kumar (2005) found that it is not sufficient to consider how much to spend on acquisition or retention alone but, instead, firms need to consider how they must balance acquisition and retention spending together to maximize profitability and double the ROI.
11. Interaction orientation: Ramani and Kumar (2008) ask, Should firms realign themselves to realize augmented CLV? If so, is interaction orientation the answer?
12. Referral marketing strategy: How can firms enhance their value through customers’ referral behavior? By implementing customized campaigns for each customer value segment, B2B and B2C firms have realized large profit gains, representing a higher ROI (Kumar, Petersen, and Leone 2007, 2010, 2013).
13. Linking CLV to shareholder value: How do firms leverage the CLV metric to drive their stock price and provide more value to their stakeholders? By linking CLV-based actions to a firm’s stock price, B2B and B2C firms have reported significant increases of approximately 35% and 57%, respectively, in their stock prices; better prediction of stock price movements; and superior performance with respect to the stock market index and rival firms (Krasnikov, Jayachandran, and Kumar 2009; Kumar and Shah 2009). products and services they consume. When firms pursue opportunities to draw out indirect profit contributions from customers, it implies engaging with customers by identifying the various sources of profit. Such a focus would result in maximizing customer engagement value. Conceptually, customer engagement value is the total value provided by customers who value the brand such that they engage with the firms ( 1) through their purchase transactions (or CLV), ( 2) through their ability to refer other customers to the firm using the firm’s referral program (or customer referral value [CRV]), ( 3) through their power to positively influence other customers about the firm’s offerings on social media (or customer influence value [CIV]), and ( 4) by providing feedback to the firm for product and service ideas (or customer knowledge value [CKV]) (Kumar et al. 2010).

CRV. With respect to indirect customer contributions to profit, promoting customer referrals is a popular practice adopted by firms. The CRV metric captures the net present value (NPV) of the future profits of new customers who purchased the firm offerings as a result of the referral behavior of the current customer (Kumar, Petersen, and Leone 2010). Kumar, Petersen, and Leone (2007) showed that when targeted referral behavior campaigns were offered to select customers of a telecommunications firm, it resulted in overall value gains of $486,090, representing an ROI of 15.4. This study established that customers who score highly on CLV are not the same as those who are successful at referring new customers, and it made the case for measuring both CLV and CRV when evaluating marketing campaigns. To compute CRV, the first step is for firms to integrate their customer transaction database with the referral database. In the absence of referral behavior information, firms can collect it from new customers by asking questions such as, “Were you referred, and if so, by whom?” Or, “To what degree did the referral influence your decision to transact with us?” The CRV metric is, however, not relevant for all business situations. For instance, customersmay not make referralsif they are not attached to the product (e.g., fast-moving consumer goods). Furthermore, a customer recommendation for one product does not necessarily apply to the firm’s portfolio of products.

The concept of referral behavior has also been extended to apply to the B2B relationship setting through the business reference value (BRV) metric. This metric computes the amount of profit the existing client firm can help generate from the prospect firms that purchase the firm offerings as a result of the client reference. Significant differences were found between high-BRV and low-BRV clients of a telecommunications and financial services firm, which indicated that, compared with low-BRV clients, high-BRV clients ( 1) contribute more value, ( 2) stay longer with the firm, ( 3) are more likely to provide a video reference than a “call me” reference, and ( 4) are larger in size and annual revenue (Kumar, Petersen, and Leone 2013). By linking CRV and BRV to CLV, firms can begin to identify the value of customers and enhance it through optimally designed marketing campaigns.

CIV. The power of the online medium has influenced customers to ( 1) persuade and convert others into customers, ( 2) continually use the firm’s offerings, and ( 3) change/modify their own purchase patterns. In an effort to conceptualize and metricize the customer influence on others, research has contributed two key metrics—customer influence effect (CIE) and CIV. Whereas the CIE measures the net spread and influence of a message from a particular individual, the CIV calculates the monetary gain or loss a firm experiences that is attributable to a customer, through his or her spread of positive or negative influence. Tracking these two metrics for a company’s social media campaign, Kumar et al. (2013) were able to demonstrate a 49% increase in brand awareness, 83% increase in ROI, and 40% increase in sales revenue growth rate. Although this study described the applicability of the CIE and CIV metrics in the case of an offline retailer, it can be extended to online retailers as well. Companies such as Starbucks and Staples already have established customer relationship management practices alongside a vibrant social media presence.

CKV. Because customer input is a valuable resource in the product development process, the value of this contribution needs to be captured and included as part of a customer’s value to the firm. This was captured in the conceptualization of the CKV metric, which refers to the monetary value attributed to a customer by a firm as a result of the profit generated by implementing an idea/suggestion/feedback from that customer. This customer feedback not only identifies the areas that are in need of improvement but also helps provide suggestions and solutions for future upgrades and modifications to firm offerings. This feedback has the potential to make the entire offering more attractive to existing and potential customers as well as improve process efficiencies. Customers must be attributed to the corresponding feedback to receive credit toward their asset value (Kumar and Bhagwat 2010).

While the value of customer feedback can be substantial to any firm, juxtaposing CLV with CKV can yield even greater insights to firms. Normally, customers with low CLVs have little experience with the product and/or they are likely to be unenthusiastic about the firm and therefore are likely to provide little feedback to the firm. Consequently, the higher a customer’s CLV, the more positive that customer will perceive the company and its products to be, and the more opportunities for the company to receive input. However, at very high levels of CLV (an indication of a close fit between the company’s products and a customer’s needs), the customers are likely to be highly satisfied and therefore have little incentive to provide feedback. These customers can, however, be encouraged to assist less experienced and less knowledgeable customers when firms implement a communication medium to do so.

Customer brand value. The three tangible value metrics—CRV, CIV, and CKV—collectively capture customers’ indirect profit contributions. In addition to these metrics, an attitudinal metric (intangible value)—customer brand value (CBV)—measures the value that the customer attaches to the brand as a result of all the marketing and communication messages delivered through different media. Conceptually, CBV refers to the total value a customer attaches to a brand through his or her experiences with the brand over time (Kumar, Luo, and Rao 2016). The CBV is a multidimensional metric that measures the customer’s brand knowledge, brand attitude, brand purchase intention, and brand behavior and enables companies to devise appropriate strategies depending on where the problem exists—awareness, trust, or repeat purchase.

Monitoring all the components of CBV becomes important from a branding standpoint. That is, brand-building efforts are aimed at inducing favorable behavior outcomes toward the brand, such as longer duration, higher purchase frequency, higher contribution margin, and positive customer referrals. Such behavioral outcomes determine the CLV scores. With this understanding, managers can work on optimizing the components of CBV to improve an individual’s CLV score. In this regard, Kumar (2013) shows that CBV is the foundation for managing CLV, CRV, CIV, and CKV.

The next component of the CVT relates to identifying the metrics to ascertain the value of customers. Firms constantly struggle with managing customers while ensuring profitability. In many cases, the cost of serving a customer may far exceed the returns from that customer. In such a scenario, firms are caught between retaining their customer portfolio/base and ensuring a healthy bottom line. The CLV metric will help firms manage healthy customer portfolios.

Direct Economic Value Contribution

As mentioned previously, the CLV stream of research has uncovered several resource reallocation insights that have helped firms maximize their value. Specifically, studies have focused on the magnitude of cash flows (the expected returns as measured by CLV) and on the volatility and variability of the cash flows (the risk element associated with customer revenue contributions). This dual focus has enabled firms to better understand customer portfolio decisions and manage customers. Through this process, it is possible to select and invest in customers who will optimize the overall customer cash flow while balancing the risk in cash flow variations.

While the concept of managing customer portfolios is similar to the financial portfolio theory, a couple of key differences must be noted. First, financial assets are typically classified in line with their historic risk/return characteristics. However, the same approach does not apply when treating customers as assets. Research has shown that past customer contributions are not highly correlated with future profit potential when using traditional or backward-looking metrics (Venkatesan and Kumar 2004). The CLV metric, in contrast, has been found to be a good predictor of future customer profitability. Second, finance managers typically invest in individual assets and try to maximize the portfolio’s return. However, marketing managers largely allocate resources at the customer segment level and increase customer profit. This is due to the nonlinear relationship between customer investments and returns. In other words, customer responses operate within certain thresholds of the level of marketing investments, and too few or too many marketing investments may not elicit the desired customer response. Whereas early studies have contributed to managing customer portfolios by optimizing segment-level risk/return (Buhl and Heinrich 2008; Reinartz and Kumar 2003; Tarasi et al. 2011), more recent studies are focusing on optimizing resource allocation at the individual customer level to maximize overall profitability (Kumar, Zhang, and Luo 2014; Luo and Kumar 2013; Petersen and Kumar 2015).

Depth of Direct Economic Value Contribution

To realize the full potential of customer contributions, firms must focus on the augmented customer value while managing customer portfolios. This is possible through optimal allocation of resources by prioritizing customers on their augmented profitability and their receptiveness to marketing efforts. To do so, decile analysis of the predicted CLV must be performed. Here, the baseline CLV (i.e., NPV of future profits that considers only the focal product category that the customer purchases) and the incremental CLV (i.e., NPV of future profits that considers customer purchases only from new product categories) are separately tracked. Conceptually, the augmented CLV is the sum of baseline CLV and the monetary impact of the portfolio of strategies discussed previously. Furthermore, the medium-value customers experience the highest gain from the portfolio of strategies—higher than even the high-value customers. This indicates the receptiveness of customers to firm-initiated marketing actions. Following this, firms can prioritize marketing resources and actions according to the impact on future profits and on firm value.

A good way to understand the impact of optimal allocation of resources is through the firm’s usage of marketing communication channels (Venkatesan, Kumar, and Bohling 2007). To optimally allocate their resources, firms must first identify their most profitable customers and those who are the most responsive to marketing efforts. Performed at the individual customer level, the selection of the best mix of communication channels is determined on the basis of the responsiveness of each customer. The cost-effectiveness of these channels is also considered to measure the customers’ potential revenue contribution based on the contacts made. The firm then decides the frequency and interval of contacts through these channels to develop a customer contact strategy. Analyzing customer behavior in relation to these factors provides firms with valuable information about their customers’ preferences and attitudes. By carefully monitoring customers’ purchase frequency, the time elapsed between purchases, and the contribution margin, managers can determine the frequency of firm initiatives to maximize CLV through an optimal contact strategy.

The benefits of optimal resource allocation using CLV as the metric can be observed even in a complex business setting. When IBM adopted the CLV metric to measure customer profitability and allocate resources for customers, the firm determined the level of contact and outreach efforts through telesales, email, direct mail, and catalogs on an individual customer basis. At the conclusion of this program (based on approximately 35,000 customers), IBM was able to effectively reallocate resources for approximately 14% of customers as well as increase revenues by about US$20 million. This was done without increasing the investment but by abandoning ineffective techniques based on customer spending history in favor of the CLV metric (Kumar et al. 2008).

Breadth of Indirect Economic Value Contribution

In managing customer portfolios, firms must realize that customers have multiple ways to contribute value, and all avenues have to be explored to maximize customer contributions. In this regard, the CLV metric has resulted in the creation of other metrics that can be used to manage and maximize customer value. These metrics must be managed independently, because a customer could contribute in one or more of these ways. Furthermore, firms must identify the ideal combination of direct and indirect value contribution from the metrics that provide the greatest profits to the firm. Figure 3 illustrates the metrics that embed the principles of CLV.

While most of the metrics illustrated in Figure 3 have been discussed in previous sections of this study, a brief description of salesperson future value (SFV), donor lifetime value (DLV), and employee engagement value (EEV) is provided here in the following subsections.

SFV. Despite numerous studies conducted on the sales function, the future value of the salesperson to the firm and how organizational factors affect it has received little attention. Recognizing the importance of this knowledge to firms, Kumar, Sunder, and Leone (2014) conceptualized the SFV metric and empirically demonstrated the short-term and long-term effects of managing SFV. The SFV is defined as the NPV of future cash flows from a salesperson’s customers (i.e., CLV) after accounting for the costs of developing, motivating, and retaining the salesperson. By measuring the value of the salespeople using the SFV metric and linking the performance with the types of training and incentives each salesperson receives, it is possible to identify the best-performing salespeople and tailor the training and incentives to maximize their performance. When this approach was implemented in a Fortune 500 B2B firm, the firm was able to reallocate training and incentive investments across salespeople, which resulted in an 8% increase in SFV across the sales force and a 4% increase in firm revenue (Kumar, Sunder, and Leone 2015).

DLV. Similar to for-profit firms, nonprofit firms also need to build relationships to be able to raise donations. In this case, the relationships are created with potential donors. Furthermore, the nonprofit firm will have to be selective about the donors with whom it builds relationships, so that it judiciously uses its limited resources. To make this possible, Kumar and Petersen (2016) conceptualized the DLV metric, which refers to the sum of donations in the future years discounted to the present value over a donor’s lifetime with the firm. Using the donor demographic information, past donation behavior, and marketing information, the DLV is modeled by predicting ( 1) the likelihood of donation; ( 2) the value of donation, given that the person will donate; and ( 3) marketing costs expended in seeking donations. By computing the DLV, it is possible for nonprofit firms to rank-order donors on the basis of their future donation value to the firm. This metric can predict who will be able to donate with 90% accuracy, the value of donations with 80% accuracy, and the DLV with 75% accuracy.

EEV. Firms can leverage the power of an engaged customer base better if they have a workforce that interacts well with customers. Customer–employee interactions help create perceptions about the firm, which affect repeat customer purchases (Sirianni et al. 2013). These perceptions lead to attitudinal and behavioral outcomes through their impact on purchases, referrals, influence, and knowledge that the customers provide to the firm. In this regard, a positive interaction between customers and employees is likely to motivate how customers talk about the brand and whether they recommend it to their friends and relatives. Kumar and Pansari (2014, p. 55) define employee engagement as “a multidimensional construct which comprises of all the different facets of the attitudes and behaviors of employees towards the organization.” The dimensions of employee engagement comprise employee satisfaction, employee identification, employee commitment, employee loyalty, and employee performance. Kumar and Pansari (2016a) developed an approach to measure EEV using a five-point scale. When implemented in the airline, telecommunication, and hotel industries, the authors found that the highest level of growth in profits (10%–15%) occurs when a company’s employees are highly engaged (i.e., have a high EEV); the lowest level of growth (0%–1%) occurs when the company’s employees are disengaged (i.e., have a low EEV). Ongoing research continues to uncover several facets of the metrics presented in Figure 3, and the insights generated thus far have advised firms in valuing customers.

Finally, the concepts and metrics discussed previously lead to the establishment of strategies that will aid in growing customer value. In developing strategies, firms are often misled by the belief that loyal customers are profitable. In addition, firms’ belief that creating customer reward programs can result in increased repeat purchasing behavior, and thereby improve firm profitability, is misplaced. Research in this area has shown that loyal customers are not necessarily profitable, and the relationship between repeat purchases and profitability is more complex than is often perceived. In addition, firms regularly use traditional metrics to measure the value of their customers, and these lead managers to implement flawed marketing strategies that drain the firm’s resources. In this regard, the CLV metric is ideal for firms aiming to grow and nurture customer profitability. When firms adopt a CLV-based approach, they can make consistent decisions over time about ( 1) which customers and prospects to acquire and retain, ( 2) which customers and prospects not to acquire and retain, and ( 3) the level of resources to spend on the various customer segments.

Direct Economic Value Contribution

When aiming to maximize the direct contribution of customers to the firm, the CVT proposes that the focus be on establishing profitable customer relationships that are based on customer transactions. In this regard, Reinartz and Kumar (2002) found that ( 1) loyal customers do not cost less to serve, ( 2) loyal customers consistently paid lower prices, and ( 2) customers who were attitudinally and behaviorally loyal were more likely to be active word-of-mouth marketers than those who were only behaviorally loyal. In effect, the study found that while there may be long-standing customers who are only marginally profitable, there also may be short-term customers who are highly profitable. The identification of the distinct customer types led to the development of the following specific strategies:

1. High repeat purchases and high profitability. Referred to as “True Friends,” these customers buy steadily and regularly over time. They are generally satisfied with the firm and are usually comfortable engaging with the firm’s processes. Firms should build relationships with these customers because they present the highest potential for long-term profitability.
2. Low repeat purchases and high profitability. Referred to as “Butterflies,” these customers do not repeat purchase often, tend to buy a lot in a short time period and then move on to other firms, and avoid building a long-term relationship with any single firm. Firms should enjoy their profits until they turn to competition.
3. High repeat purchases and low profitability. Referred to as “Barnacles,” these customers, if managed unwisely, could drain the company’s resources. Firms must evaluate their size and share of wallet. If the customers’ share of wallet is low, firms can up-sell and cross-sell to them to make them profitable. However, if the size of wallet is small, strict cost control measures must be taken to prevent losses for the firm.
4. Low repeat purchases and low profitability. Referred to as “Strangers,” these customers not only are a poor fit to the company but offer very little profit potential to the firm. Firms should identify these customers early on and avoid any investment toward building a relationship with them.

A relationship focus would lead firms to identify those customers who provide the most value and prioritize the marketing efforts accordingly.

Depth of Direct Economic Value Contribution

Firms are expected to demonstrate the profitability of their marketing actions at the individual customer level on an ongoing basis. At the same time, customers expect firms to customize products and services to meet their demands. A successful management of these two types of expectations depends on the firms’ ability to better interact with customers and create a unique positioning in the future. This calls for implementing an interaction strategy in which firm–customer and customer–customer interactions constantly occur. In other words, the CVT advocates for a firm–customer exchange environment that focuses on constant interactions as opposed to just profitable transactions.

Implementing the interaction strategy requires the firm to adopt an interaction-oriented approach, which consists of four components—customer concept, interaction response capacity, customer empowerment, and customer value management—that the firm can utilize to increase the impact on its profitability (Ramani and Kumar 2008). First, the customer concept proposes that the unit of every marketing action or reaction is an individual customer. This places the customer at the top of the hierarchy in the customer–firm relationship. By doing so, firms are able to observe customer behavior and respond appropriately. Second, the interaction response capacity (the degree to which a firm can provide successive products or services drawing on previous customer feedback) highlights the importance of firms attending to and promptly addressing customer needs. Third, the customer empowerment component refers to the extent to which a firm allows its customers to ( 1) connect with the firm and design the nature of the transaction and ( 2) connect and collaborate with each other by sharing information, praise, and criticism about a firm’s product and services. Finally, the customer value management component refers to the extent to which a firm can quantify and calculate the individual customer value and use it to reallocate resources to customers. Firms such as IBM and American Express, through their endorsement of practices that are consistent with the elements of interaction orientation, have realized superior business performance, thereby demonstrating the managerial significance of the interaction orientation approach.

Breadth of Indirect Economic Value Contribution

Firms can nurture customer profitability by implementing an engagement strategy that considers the value generated by customers and employees and results in superior firm performance. The CVT proposes an engagement strategy that builds an engaged and committed customer and employee base to enhance the firm’s overall profitability (Kumar and Pansari 2016a).

Although the engagement approach is powerful by itself, its true potential is realized when it is viewed from a long-term perspective. Engaged customers contribute to the long-term reputation and recognition of the brand. Creating an environment where customers are more engaged with the company may require an initial investment, but doing so has the potential to generate higher profits in the long run through the creation of customer engagement (Verhoef, Reinartz, and Krafft 2010). Furthermore, fostering engagement within firms is effective even in a recessionary economy. In a recessionary period, firms face budgetary challenges that significantly affect their marketing plans, which influences their levels of brand awareness and adoption. During this period, firms can mitigate the risks posed by the dents in their marketing budgets if they have a highly engaged employee base that promotes the firm’s brand and its products/services to its customers. This would ensure delivery of a superior customer experience, thereby increasing customer purchases, influence, and referrals—all without any additional marketing investments.

The true measure of any marketing strategy or initiative is the improved financial result for the firm implementing it. This is why it is critical to study customer reactions to firm actions. When firms can precisely link their actions to customer value and, ultimately, to firm/shareholder value, they can begin to realize the potential of valuing customers as assets. Using concepts from economics, finance, and marketing, this article has created an interface that connects the value of each customer (determined by evaluating the lifetime value of the customer to the firm) with the performance and, therefore, the valuation of the firm. This connection has been established by ( 1) valuing customers as assets,

( 2) managing a portfolio of customers, and ( 3) nurturing profitable customers. Each of these tactics plays a unique role in the optimization of shareholder value, customer equity, and overall profitability, and each tactic also works in combination with others to increase the overall impact on the firm value. Specifically, the CVT proposed here provides the following benefits:

1. Benefit to the firms: When firms understand customer profitability and adopt CVT as the desired approach, they will be able to ( 1) attract and retain the most valuable customers, ( 2) nurture customers into a skilled resource base for the firm, ( 3) prevent their customers from switching to competitors by instilling a heightened sense of ownership of the firm among its customers, ( 4) consistently evolve their product/service offerings and match it to customer needs and preferences, ( 5) develop the ability to accurately foresee customer responses, and ( 6) exhibit superior aggregate business-level performance because the firm will be dynamically maximizing the profit function at every stage of business activity across all customers.
2. Benefit to the customers: The CVT essentially advocates that the individual customer be the unit of analysis of every marketing action and reaction. This implies that firms respond to heterogeneous customers differently at different points in time by pooling information from multiple sources and points in time. This approach provides customers avenues to ( 1) connect with the firm and actively shape the nature of transactions and ( 2) connect and collaborate with each other by sharing information and knowledge. Furthermore, customers will be subjected to only the product/service offerings that are appropriate to them rather than every offering in the firm’s product portfolio. This keeps the customers focused, involved, and connected to the firm, thereby increasing their lifetime with the firm.
3. Benefit to the environment: When firms base their marketing actions on the potential value the customers can bring them, it becomes easier to align and allocate the optimal amount of resources toward each customer. For instance, firms can plan and optimize the printing and mailing of marketing materials only to those customers they are intended for, and not send mass mailers. In addition to enhancing the effectiveness of the firm’s marketing efforts, this would also prevent the wasteful usage of valuable environmental and infrastructural resources and would help firms embrace sustainability as part of their core mission (Kumar and Christodoulopoulou 2014).
4. Benefit to society: The benefits to society from implementing the CVT are threefold. First, there is a clear line of communication from firms to customers in terms of what to expect. The customer expectations relate to product/service offerings as well as firm responses through marketing actions. Second, the customer’s repeat purchases can now be consolidated among a few preferred firms. When firms display their respective needs and expectations, customers’ repeat purchases gradually are aligned to matching firms. Over time, a self-selection of repeat patronage by firms takes place that leads firms to focus their resources to satisfy their customers’ expectations. Finally, customers become empowered, such that they can exercise their choice and free will, thereby having a definite say in the marketing transaction process. This empowerment is evident through behavior ranging from customer advocacy (in case of complete satisfaction of firm offerings) to customer boycott and negative word of mouth (in case of extreme displeasure).
5. Benefit to the employees: Employees are instrumental in providing a better customer experience, and this leads to customer engagement. Firms can engage their customers only if their employees are committed to delivering the brand values and performing to the best of their ability. Employees can be committed to the organization only if they understand its goals and their responsibilities toward achieving these goals. The employees must be highly engaged with the firm to provide peak performance (Kumar and Pansari 2014). Therefore, in addition to engaging customers, firms must also ensure that they are engaging their employees.

The insights from various financial theories served as a good starting point in the development of the CVT. In addition, the modern portfolio theory provided insights into risk diversification and the maintenance of a balanced portfolio. Overall, this study has showcased CVT as a forward-looking approach to aid managers in the valuation and management of a customer’s future contributions.

Specifically, two key managerial implications emerge. First, the applicability of the CVT has been demonstrated in various scenarios spanning multiple markets (e.g., B2B, B2C), business settings (e.g., contractual, noncontractual), regional contexts (e.g., domestic, global), and industries. This should provide confidence to managers regarding its relevance and applicability to the current marketplace. Furthermore, experts opine that in the next five to ten years, CLV will be the metric of prime importance to businesses (Fader 2016). Second, given the evolution in the methodology to value customers, the availability of customer information, and the 360-degree customer data, managers are enriched with improvements that can result in better implementation. Despite the success of the CLV metric, it has not experienced industry-wide acceptance. For instance, a survey found that while 76% of the respondents agreed that CLV was important to their organization, only 42% reported that they were able to implement it (Charlton 2014).

In this light, I identify three areas that can benefit from future research. First, the identification of the relevant organizational structure required to facilitate and implement CVT-based strategies is important. A customer-centric organization has been identified as a basis to align an organization (Kumar 2013). Such an alignment can enable firms to view their customers both as a source of business and as a potential business resource. However, more insights are necessary to better understand the organizational requirements for a successful CVT implementation. Second, the role of multiple stakeholders needs to be explored. For instance, studies have investigated the circumstances in which firm–stakeholder relationships are forged, such as those in which ( 1) the stakeholder is critical to the functioning of the firm (e.g., institutional investors having an equity interest in a firm), ( 2) the stakeholder aims to gain from the relationship (e.g., customers seeking firm offerings), ( 3) the firm and the stakeholder mutually gain from the association (e.g., the firm and the channel partners working collectively to produce and sell offerings), and ( 4) the stakeholder has moral rights attached to the firm’s operations (e.g., customer rights and employee rights) (Freeman and Reed 1983). However, precise recommendations have to be generated that can inform firms about better stakeholder integration for a successful implementation of CVT. Finally, the time-varying effects of certain drivers involved in the implementation of CVT need to be better understood. These elements include the valuation methodologies, data availability, intensity of customer-level data, and emergence of smarter consumers. Over time, these elements are likely to have a dynamic effect on CVT-based strategies. In summary, by focusing on a CVT-based approach, firms can ( 1) acquire and retain profitable customers, ( 2) employ resources productively, and ( 3) nurture profitable customers, which will ultimately result in higher firm value.

Breadth of Indirect Economic Value Contribution

Apart from customers’ own contributions, the CLV metric also applies to customers’ indirect profit contributions to the firm such as their referral behavior, online influence on prospects’ and other customers’ purchases, and review/feedback on the

1 “Firm offering” refers to physical goods, services, brands, or a combination thereof.

2 “Customer” denotes both an end consumer and a business customer.

3 I recognize that the valuation of a company is affected by many other factors (including nonmonetary factors such as competition and mergers and acquisitions) beyond the focal firm’s cash flows. However, this example is designed to illustrate the importance of cash flows in determining firm value. I thank an anonymous reviewer for raising this distinction.

4 Exceptions to this are initial public offerings wherein the stock value increases as new customers are added.

5 Although the prediction of future contribution margin and future costs does generate risk in the CLV calculation, a higher discount rate can be used to account for the uncertainties in the prediction.

6 In the B2B markets, some categories may not conform to the three-year window (e.g., capital goods such as plant and machinery).

DIAGRAM: FIGURE 1 Differences in Valuing Stocks Versus Customers

DIAGRAM: FIGURE 2 Understanding the Link Between CVT and Firm Value

DIAGRAM: FIGURE 3 Maximizing Indirect Value Contribution

PHOTO (BLACK & WHITE)

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Acknowledgments 

Outstanding Reviewers

The Editors are pleased to honor these Editorial Review Board members who distinguished themselves by virtue of their review quality, constructiveness, timeliness, and workload. Join us in congratulating this global network of scholars for their outstanding contribution as reviewers to the Journal of Marketing between July 1, 2019 and June 30, 2020.

Kimmy Chan, Hong Kong Baptist University

Eileen Fischer, York University

Robert Fisher, University of Alberta

Colleen Harmeling, Florida State University

Michael Hui, University of Macau

Kartik Kalaignanam, University of South Carolina

Son Lam, University of Georgia

Jongkuk Lee, Ewha Womans University

Barak Libai, IDC Herzliya

Yuping Liu-Thompkins, Old Dominion University

Ashwani Monga, Rutgers University

Lisa Scheer, University of Missouri

Nita Umashankar, San Diego State University

Ralf van der Lans, Hong Kong University of Science and Technology

The Journal of Marketing confers three awards focused on different types of contribution to the field of marketing.

2020 Sheth Foundation/Journal of Marketing Award: The award honors the JM article that has made long-term contributions to the field of marketing. An article is eligible for the award in the fifth year after its publication.

Winner: Frank Germann, Peter Ebbes, and Rajdeep Grewal (2015), "The Chief Marketing Officer Matters!" 79 (3), 1–22.

Finalists:

• Ya You, Gautham G. Vadakkepatt, and Amit M. Joshi (2015), "A Meta-Analysis of Electronic Word-of-Mouth Elasticity," 79 (2), 19–39.
• Sourindra Banerjee, Jaideep C. Prabhu, and Rajesh K. Chandy (2015), "Indirect Learning: How Emerging-Market Firms Grow in Developed Markets," 79 (1), 10–28.
• Christian Homburg, Martin Schwemmle, and Christina Kuehnl (2015), "New Product Design: Concept, Measurement, and Consequences," 79 (3), 41–56.
• Christoph Fuchs, Martin Schreier, and Stijn M.J. Van Osselaer (2015), "The Handmade Effect: What's Love Got to Do with It?" 79 (2), 98-110.

2019 Shelby D. Hunt/Harald H. Maynard Award: The award honors the JM article that has made the most significant contribution to marketing theory in a given calendar year.

Winner: Sven Molner, Jaideep C. Prabhu, and Manjit S. Yadav (2019), "Lost in a Universe of Markets: Toward a Theory of Market Scoping for Early-Stage Technologies," 83 (2), 37–61.

Finalists:

• Caleb Warren, Rajeev Batra, Sandra Maria Correia Loureiro, and Richard P. Bagozzi (2019), "Brand Coolness," 83 (5), 36–56.
• Katherine White, Rishad Habib, and David J. Hardisty (2019), "How to SHIFT Consumer Behaviors to be More Sustainable: A Literature Review and Guiding Framework," 83 (3), 22–49.
• Allègre L. Hadida, Jan B. Heide, and Simon J. Bell (2019), "The Temporary Marketing Organization," 83 (2), 1–18.

2019 AMA/Marketing Science Institute/H. Paul Root Award: The award honors the JM article that has made the most significant contribution to the advancement of the practice of marketing in a calendar year.

Winner: Karen Page Winterich, Gergana Y. Nenkov, and Gabriel E. Gonzales (2019), "Knowing What It Makes: How Product Transformation Salience Increases Recycling," 83 (4), 21–37.

Finalists:

• Alexander Bleier, Colleen M. Harmeling, and Robert W. Palmatier (2019), "Creating Effective Online Customer Experiences," 83 (2), 98–119.
• Adithya Pattabhiramaiah, S. Sriram, and Puneet Manchanda (2019), "Paywalls: Monetizing Online Content," 83 (2), 98–119.
• Gerard J. Tellis, Deborah J. MacInnis, Seshadri Tirunillai, and Yanwei Zhang (2019), "What Drives Virality (Sharing) of Online Digital Content? The Critical Role of Information, Emotion, and Brand Prominence," 83 (2), 98–119.

Thank you to the Article Award Committees:

• Sheth Foundation/Journal of Marketing Award: Roland T. Rust (chair), Kusum L. Ailawadi, and Ruth N. Bolton
• Shelby D. Hunt/Harold H. Maynard Award: Robert W. Palmatier (chair), Inge Geyskens, and Deborah J. MacInnis
• AMA/Marketing Science Institute/H. Paul Root Award: Harald van Heerde (chairperson), John G. Lynch, and Scott A. Neslin

The following scholars served as guest Associate Editors between July 1, 2019 and June 30, 2020 for the Journal of Marketing. We appreciate their investments of time and energy to support JM's mission to develop and disseminate knowledge about real-world marketing questions relevant to scholars, educators, managers, policy makers, consumers, and other societal stakeholders around the world.

Hans Baumgartner, Pennsylvania State University

Barry Bayus, University of North Carolina-Chapel Hill

Sundar Bharadwaj, University of Georgia

Lauren Block, Baruch College

Simona Botti, London Business School

Pierre Chandon, INSEAD

Amar Cheema, University of Virginia

Keisha Cutright, Duke University

Peter Danaher, Monash University

John Deighton, Harvard University

Benedict Dellaert, Erasmus University

Kristin Diehl, University of Southern California

Fred Feinberg, University of Michigan

Eileen Fischer, York University

Kent Grayson, Northwestern University

Zeynep Gurhan-Canli, Koç University

Gerald Häubl, University of Alberta

Mark Houston, Texas Christian University

G. Tomas M. Hult, Michigan State University

Satish Jayachandran, University of South Carolina

Barbara Kahn, University of Pennsylvania

P.K. Kannan, University of Maryland

Robert Kozinets, University of Southern California

Anja Lambrecht, London Business School

Gary Lilien, Pennsylvania State University

John G. Lynch Jr., University of Colorado

Kelly Martin, Colorado State University

Carl Mela, Duke University

David Mick, University of Virginia

Vikas Mittal, Rice University

Wendy Moe, University of Maryland

Maureen Morrin, Rutgers University

Vicki Morwitz, New York University

Brian Murtha, University of Kentucky

Thomas P. Novak, George Washington University

Steven Nowlis, Washington University in St. Louis

Brian Ratchford, University of Texas at Dallas

Werner Reinartz, University of Cologne

Aric Rindfleisch, University of Illinois at Urbana–Champaign

Lisa Scheer, University of Missouri

Maura Scott, Florida State University

Sankar Sen, Baruch College

Venky Shankar, Texas A&M University

John Sherry Jr., University of Notre Dame

Sanjay Sood, University of California, Los Angeles

Craig Thompson, University of Wisconsin-Madison

Kapil Tuli, Singapore Management University

Stijn van Osselaer, Cornell University

Katherine White, University of British Columbia

Carolyn Yoon, University of Michigan

Acknowledgments 

Outstanding Reviewers

The Editors are pleased to honor these Editorial Review Board members who distinguished themselves by virtue of their review quality, constructiveness, timeliness, and workload. Join us in congratulating this global network of scholars for their outstanding contribution as reviewers to the Journal of Marketing between July 1, 2018 and June 30, 2019.

Kersi Antia, Western University

Neeraj Arora, University of Wisconsin–Madison

Lauren Block, Baruch College

Marc Fischer, University of Cologne

Ashlee Humphreys, Northwestern University

Detelina Marinova, University of Missouri

Kelly Martin, Colorado State University

Brian Murtha, University of Kentucky

Tom Novak, George Washington University

Jaideep Prabhu, Cambridge University

S. Sriram, University of Michigan

Claudia Townsend, University of Miami

Kapil Tuli, Singapore Management University

The following scholars served as guest Associate Editors over the last year for the Journal of Marketing. We appreciate their investments of time and energy to support JM's mission to develop and disseminate knowledge about real-world marketing questions relevant to scholars, educators, managers, policy makers, consumers, and other societal stakeholders around the world.

Jennifer Argo, University of Alberta

Neeraj Arora, University of Wisconsin–Madison

Hans Baumgartner, Pennsylvania State University

Sundar Bharadwaj, University of Georgia

Lauren Block, Baruch College

Simona Botti, London Business School

Pierre Chandon, INSEAD

Alexander Chernev, Northwestern University

Darren Dahl, University of British Columbia

Peter Danaher, Monash University

John Deighton, Harvard University

Kristin Diehl, University of Southern California Marshall

Fred Feinberg, University of Michigan

Eileen Fischer, York University

Marc Fischer, University of Cologne

Peter Golder, Dartmouth College

Kent Grayson, Northwestern University

Zeynep Gurhan-Canli, Koç University

Michael Haenlein, ESCP Europe

Gerald Häubl, University of Alberta

Timothy Heath, University of South Florida

Mark Houston, Texas Christian University

John Hulland, University of Georgia

G. Tomas Hult, Michigan State University

Satish Jayachandran, University of South Carolina

George John, University of Minnesota

Deborah Roedder John, University of Minnesota

P.K. Kannan, University of Maryland

Robert Kozinets, University of Southern California

V. Kumar, Georgia State University

Robert Leone, Texas Christian University

Gary Lilien, Pennsylvania State University

John Lynch, University of Colorado

Detelina Marinova, University of Missouri

Kelly Martin, Colorado State University

David Mick, University of Virginia

Natalie Mizik University of Washington

Wendy Moe, University of Maryland

Maureen Morrin, Temple University

Vicki Morwitz, Columbia University

Brian Murtha, University of Kentucky

Stephen Nowlis, Washington University

Akshay Rao, University of Minnesota

Brian Ratchford, University of Texas Dallas

Werner Reinartz, University of Cologne

Aric Rindfleisch, University of Illinois Urbana–Champaign

Lisa Scheer, University of Maryland

Sankar Sen, City University of New York

Venkatesh Shankar, Texas A&M University

John Sherry Jr., University of Notre Dame

Andrew Stephen, University of Oxford

Craig Thompson, University of Wisconsin

Kapil Tuli, Singapore Management University

Michel Wedel, University of Maryland

Katherine White, University of British Columbia

Sha Yang, University of Southern California

An Empirical Analysis of the Joint Effects of Shoppers’ Goals and Attribute Display on Shoppers’ Evaluations 

This article develops a decision-making framework that highlights how display of numeric attribute information (e.g., display of calorie information) and shoppers’ goals (i.e., having a diet focus vs. a taste focus) jointly influence shoppers’ choices and preferences. Across two sets of studies, including a field study involving the launch of a new Coca-Cola product, the authors show that when food items are displayed in an aligned manner (i.e., when food items with lower-value calorie information are displayed below food items with higher calorie values), shoppers assign more importance weight to calorie gap information. In turn, higher importance weight assigned to calorie gap information leads diet-focused shoppers to relatively prefer low-calorie food items but leads taste-focused shoppers to relatively prefer higher-calorie food items. The third set of studies shows that this decision-making framework has widespread applicability and is relevant in any domain in which advertising, retail, and online displays show comparisons of numeric attribute information.

In mid-2014, Coca-Cola launched Cola-Cola Life in Sweden, a reduced-calorie cola drink that differs from zero-calorie diet colas because it does not contain aspartame (which many perceive as unhealthy; Dean 2014). Reacting to an increased health focus among shoppers, launching Coca-Cola Life was part of Coca-Cola’s efforts to reduce the average calorie content of its drinks portfolio while still avoiding the use of aspartame. Some months prior to the launch, one of the authors of this article was in discussions with Coca-Cola managers about which factors were likely to influence shoppers’ choices between regular Coke and Coca-Cola Life. One factor discussed was in-store signage. Would signage indicating that Coca-Cola Life has lower calorie content be effective, despite prior research showing that merely indicating calorie values does not automatically lead shoppers to make healthier choices (Loewenstein 2011; see also Haws, Davis, and Dholakia 2016). This article was motivated, in part, by these discussions and the real-world challenges the authors (and firms like Coca-Cola) aim to examine.

To better understand the factors that influence the choice of healthier food items, we start with the foundational notion that shoppers’ food choices depend on both individual differences among shoppers and the presentation format for the nutritional information (Haws, Davis, and Dholakia 2016; Loewenstein 2011; Mohr, Lichtenstein, and Janiszewski 2012). Most prior work has focused on evaluations of single food items (e.g., Gomez, Werle, and Corneille 2017; Graham and Mohr 2014; Mohr, Lichtenstein, and Janiszewski 2012); in contrast, in this article, we consider how shoppers choose among multiple food items, which represents a normative shopping situation. For example, shoppers often choose among various entrees in restaurants, multiple soup cans in supermarkets, or numerous soft drinks in convenience stores.

We focus on two specific drivers of food item choice. We start by examining shoppers’ goals, which may be conceptualized as either individual differences or differences primed by the product category or shopping environment (e.g., Escaron et al. 2013; Newman, Howlett, and Burton 2014). A significant amount of prior research has highlighted goals related to dietary restraint, that is, the extent to which shoppers have diet goals which leads them to prefer food items with fewer calories1 (Howlett et al. 2012; Visschers, Hess, and Siegrist 2010) and consume fewer calories (Cavanagh and Forestell 2013). In this article, we offer a different proposal, making two points. First, rather than posit that shoppers are more or less focused on diet goals, we posit that shoppers focus on diet goals versus taste goals. Second, building from work on the “unhealthy = tasty” intuition (Raghunathan, Naylor, and Hoyer 2006), we argue that tastefocused shoppers intuit that unhealthy food items will better satisfy their taste goals, so their choice decisions appear to favor food items with more calories. Thus, we take a different perspective than prior work, and so we contrast shoppers with diet goals versus taste goals.

Next, in grocery store settings, choices often involve a comparison between a focal, healthy food item and a comparison food item (Suri et al. 2012); in response, food manufacturers often provide comparisons that highlight the “nutritional gap.” For example, Better’n Peanut Butter Banana spread advertises that it has “40% fewer calories,” and Trop50 orange juice proclaims that it has “50% less sugar and calories,” relative to comparable products. However, in many cases, only calorie information appears in the front-of-pack (FOP) information, so shoppers must perform calorie gap calculations themselves. In turn, building on work in numeric cognition (e.g., Biswas et al. 2013) and heuristics (e.g., Shah and Oppenheimer 2007), we propose that differences in the vertical display of food items could prompt differences in perceptions of the importance of the calorie gap. If a focal food item, with fewer calories, appears below another food item (i.e., if the focal food item is displayed in an aligned manner), then the subtraction task to calculate the calorie gap is easier. And if calculating the calorie gap is easier, then shoppers are likely to attach more importance to calorie gap information during their evaluations. Thus, displaying a focal food item in an aligned manner should increase the importance weight that shoppers place on the calorie gap. Among shoppers with diet goals, this increased importance weight shifts shoppers’ choices toward the focal food item, but among tastefocused shoppers, this increased importance weight may shift the choice share away from the focal food item, toward the higher-calorie option. By combining these propositions, we posit that when food items are displayed in an aligned (vs. nonaligned) manner, diet-focused shoppers relatively prefer lower-calorie food items, but taste-focused shoppers make choices as if they relatively prefer higher-calorie food items. This nonintuitive proposition is the central hypothesis of our article, and we test it across multiple studies, including a field study involving the choice between regular Coca-Cola and the lower-calorie Coca-Cola Life.

More generally, in this article we focus on product domains for which advertising, retail, and online displays show comparisons of numeric attribute information. The food domain is one such domain, which involves comparisons of numeric nutritional information relating to calories (and sodium). Other exemplar domains that involve comparisons of numeric attribute information include ( 1) product price comparisons, as frequently seen in basket comparisons posted in supermarkets (e.g., Publix vs. Walmart), in online comparative advertisements (Dyson vs. Shark vacuum cleaners), and on price comparison tools (e.g., hotel rates on Trivago.com); ( 2) advertisements for robotic vacuum cleaners (e.g., Neato vs. Roomba), which involve attributes such as operating time and charging time; ( 3) advertisements for cellular networks (e.g., T-Mobile vs. Verizon), which involve attributes such as Internet speed; and ( 4) advertisements for cell phones (e.g., Apple iPhone vs. Samsung Galaxy), which involve attributes such as screen size, standby time, and talk time.

In domains involving numeric attribute information, we investigate the impact of two factors on shoppers’ choice decisions. First, numeric attribute information may be displayed in an aligned (vs. nonaligned) manner, whereby aligned display involves showing the low-value numeric information below the higher-value numeric information (e.g., lower prices displayed below higher prices). Second, shoppers may have different goals, perceiving attributes as either more-is-better (MIB; preferring items with higher attribute values) or less-is-better (LIB; preferring items with lower attribute values). For example, shoppers who view price as a measure of sacrifice perceive price as an LIB attribute, whereas those who view price as a measure of quality perceive price as an MIB attribute (Dodds, Monroe, and Grewal 1991; see also Miyazaki, Grewal, and Goodstein 2005). In the domain of robotic vacuum cleaners, operating time is an MIB attribute, but charging time is an LIB attribute; in the domain of laptops, many would perceive battery life as an MIB attribute but perceive laptop weight as an LIB attribute. Building from the central hypothesis outlined previously, we posit that when items are displayed in an aligned (vs. nonaligned) manner, shoppers who perceive the displayed attribute as an LIB (MIB) attribute will relatively prefer the item with the lower (higher) value attribute.

We aim to make the following contributions. Generally speaking, we outline a parsimonious framework that examines how shoppers react to advertising, retail, or online displays in (a wide variety of) product domains involving numeric attribute information. This article identifies two elements that jointly determine shoppers’ reactions: ( 1) whether shoppers view the displayed attribute as an LIB attribute or an MIB attribute, and ( 2) whether the attribute information is displayed in an aligned or nonaligned manner, which influences the importance weight shoppers put on this attribute information. We suggest that firms can use this framework to better design advertising, retail, and online displays. While Biswas et al. (2013; work on the subtraction principle) provide an initial examination of aligned (vs. nonaligned) displays, their stated process mechanism does not easily extend beyond the price promotion domain and was examined only in LIB contexts. The current article substantially modifies and broadens the process mechanism underlying the subtraction principle to allow it to extend into multiple product domains. Moreover, we explicitly contrast LIB versus MIB contexts, outlining exactly how shoppers’ attribute perceptions (i.e., LIB vs. MIB) and display alignment jointly influence their choices and perceptions.

In addition, we aim to make two contributions specific to the food domain. Not only is the food domain important from both a firm perspective and a shopper perspective, but also the growing importance of how best to motivate people to make healthy food choices has prompted increased research focus in this domain. First, prior research suggests that people who have less focus on diet goals pay less attention to calorie information (e.g., Bialkova, Sasse, and Fenko 2016; Cavanagh and Forestell 2013; Mohr, Lichtenstein, and Janiszewski 2012). In contrast, we show that people who have less focus on diet goals (i.e., have more focus on taste goals) indeed pay attention to calorie information (similar to people with more focus on diet goals), but because of the unhealthy = tasty intuition, these shoppers behave as if they prefer food items with more calories (unlike people with more focus on diet goals). Second, as a novel point not evidenced in prior research, we show that whether shoppers make goal-consistent choices is contingent on whether food items are displayed in an aligned manner. Next, we develop our propositions and test them across multiple product domains, including in a field study involving the choice between regular Coca-Cola and the newly launched, low-calorie Coca-Cola Life.

Shoppers’ Goals During Food Item Choices

What goals do shoppers have when they make food item choices? To answer this question, we turn to literature at the intersection of food-related research and research into goals. On the one hand, there may be individual differences (i.e., “trait differences”) across shoppers, and these differences should lead to shoppers having different goals when making food item choices. Specifically, some shoppers have diet goals (Herman and Polivy 2004; Howlett et al. 2012; Naylor, Droms, and Haws 2009), so they prefer food items with fewer calories or less sodium (LIB behavior).

Prior research has examined the extent to which shoppers focus on diet goals (Haws, Davis, and Dholakia 2016; Mohr, Lichtenstein, and Janiszewski 2012; Naylor, Droms, and Haws 2009; Van Herpen and Van Trijp 2011), and the subsequent impact on food item choices and consumption. This prior research has indicated that people who have less focus on diet goals tend to pay less attention to nutritional information. Thus, Mohr, Lichtenstein, and Janiszewski (2012; p. 66) show that when presented with less healthy versus more healthy food items, people with higher levels of dietary concerns were significantly more likely to choose the healthier food item, but people with low levels of dietary concerns were relatively indifferent across food items (“significantly higher purchase intentions … for all values of dietary concern above 3.80” vs. “no significant differences below … the Johnson–Neyman point”). Similarly, Cavanagh and Forestell (2013; p. 508) found that restrained eaters consumed more (relatively healthy) Kashi cookies than Nabisco cookies, “whereas the unrestrained eaters did not differ in their consumption of the two brands,” Finally, Bialkova, Sasse, and Fenko (2016; p. 44) asked participants to choose between (relatively healthy) cereal bars and (relatively unhealthy) potato chips. They found that “consumers highly concerned about health preferred to buy cereal bars ( p = .018), while less concerned consumers selected to buy chips and cereals with equal probability ( p > .4).” Taken together, these findings appear to indicate that people with low levels of dietary concerns are relatively indifferent between the less healthy food item and the healthier food item. In this article, however, we posit differently, and so we make two distinct points.

Shoppers’ goals. Rather than describing shoppers’ goals on a continuum anchored by more versus less focus on diet goals, in this article we propose that the relevant anchors should be diet goals versus taste goals. As a novel point, we emphasize the explicit presence of taste goals (and not just less focus on diet goals), consistent with Andrews, Netemeyer, and Burton’s (1998) use of a taste goal prime as a control condition.

Prior literature has indicated that shoppers with more focus on diet goals pay attention to nutrition information (“consumers who expressed a great concern for … dietary eating … made more active use of the health label information”; Bialkova, Sasse, and Fenko 2016, p. 40), and so are more likely to prefer food items with fewer calories. However, distinct from prior literature, we propose that shoppers with less focus on diet goals (i.e., those with taste goals) also pay attention to nutrition information but use it differently, such that they choose food items with more calories. We clarify that we are not claiming that shoppers with taste goals deliberately seek out food items with more calories; rather, we suggest that these shoppers intuit that high-calorie food items are tastier (unhealthy = tasty intuition); in their quest for taste, they select relatively highercalorie food items.

To make this prediction, we build from research into behavioral traits (related to food preferences, and related to impulsivity) and perceptions of food. First, we build from work that connects dietary restraint to impulsiveness. The work of Van Koningsbruggen, Stroebe, and Aarts (2013; Table 1; p. 83) indicates that those low on dietary restraint are more likely to be impulsive. Second, more impulsive people are likely to prefer tasty food, and they are both more likely to pick up (tasty) cookies (Ramanathan and Menon 2006; Figure 3, p. 638) and more likely to choose (tasty) cake over salad (Sengupta and Zhou 2007; Study 2, p. 301). Third, unhealthy foods are more likely to be perceived as tasty (see work on the unhealthy = tasty intuition2 [Mai and Hoffmann 2015; Raghunathan, Naylor, and Hoyer 2006). Overall, we posit that people low on dietary restraint (i.e., those with taste goals) may behave as if they prefer (relatively) unhealthy food items.

Individual differences versus state differences. Beyond just individual differences (e.g., extent of diet intentions), environmental factors may also prompt differences in (diet vs. taste) goals, which we term “state differences.” Product category differences may trigger differences in goals, with shopping for health-focused foods potentially triggering diet goals and shopping for candy potentially triggering taste goals. Advertising differences may also prompt differences in goals. Foods advertised as health foods or diet foods, or foods packaged reflecting “greenness” may trigger diet goals, whereas foods advertised as comfort foods may prompt taste goals. Thus, it is possible that the same person may have diet goals when examining a certain type of food item and yet may have taste goals when examining a different type of food item.

These discussions suggest that those with diet goals should prefer lower calorie food items, whereas those with taste goals should (in line with the unhealthy = tasty intuition) behave as if they seek out higher-calorie food items. Next, we propose that differences in how food items are displayed affect the extent to which shoppers’ goals influence food item preferences, and we elaborate on this point in the following subsection.

TABLE: TABLE 1 Johnson–Neyman Regions in Study 1

aMean-centered values for diet intentions scale.

Notes: This table illustrates the conditional effect of alignment on choice of Coca-Cola Life, at values of diet intentions scale. LLCI = lower-limit confidence interval; ULCI = upper-limit confidence interval. Boldfaced cells indicate significance.

Food items are only seldom evaluated in isolation; instead, such evaluations often involve a comparison of a focal food item with potential alternatives (Suri et al. 2012). In many cases, nutritional information is available FOP, so shoppers can calculate the attribute gap (e.g., calorie gap, sodium content gap). For example, a grocery shopper aiming to buy cereal may find a focal food item, which has fewer calories, displayed either below or above a higher-calorie food item. Both food items display calorie content FOP. If the propensity to engage in calorie gap calculations depends on whether the food items are presented in an aligned (vs. nonaligned) display, then such display differences may influence food item choice. We first consider whether differences in the display format influence shoppers’ propensity to initiate a subtraction task to calculate the calorie gap, then we discuss how this propensity might influence the importance weight shoppers attach to the calorie gap for their evaluations.

Impact on propensity to initiate the subtraction task. In general, a comparison of two attributes that feature numeric information involves subtraction (Thomas and Morwitz 2009). However, prior research has not fully explored how presenting a smaller number below versus above a larger number influences subtraction calculations. We integrate research in numeric cognition with pricing research to examine this question. First, in the subtraction task A - B, A is the minuend, and B is the subtrahend. People generally perceive that it is normative to present a larger minuend above a smaller subtrahend, and prior research has affirmed that fewer computational errors occur with this format (Fuson and Briars 1990). Second, in a study of how people verify addition problems, Yip (2002) finds that inaccurate equations that fail to conform to conventional presentation norms are perceived as harder to verify as correct (e.g., it is more difficult to determine whether 7 = 3 + 5 is correct than whether 3 + 5 = 7 is correct). Accordingly, we posit that subtraction equations in which a smaller-value subtrahend appears below (above) the minuend are easier (harder) to verify. Third, because people do not like to work on overly challenging problems (Oppenheimer 2008), locating a smaller-value subtrahend above the minuend—contrary to the norm in contexts involving difference calculations—may reduce the propensity to perform a subtraction task. In research on price promotions, Biswas et al. (2013) propose the subtraction principle, a somewhat similar information processing sequence. They proposed that when sale prices are displayed to the right of the original price (i.e., smaller number to right of the larger number), shoppers perceive the subtraction task as cognitively easier and so are more likely to calculate the discount depth. However, if sale prices appear to the left of the original price, shoppers perceive the subtraction task as cognitively harder and so are less likely to initiate a subtraction task. Rather, shoppers would approximate discount depth at around 10%–12% (reflecting a discount depth benchmark from Blair and Landon [1981]).

Now assume that two (competing) cereals explicitly provide FOP calorie information. Building on the previous arguments, if the focal, healthy cereal is displayed in an aligned manner, then shoppers can calculate the calorie difference relatively easily. But if the focal cereal is displayed in a nonaligned manner, shoppers may perceive the subtraction task as harder and so may be less likely to initiate the subtraction task to calculate the calorie gap.

Importance weight attached to the calorie gap during evaluations. During evaluations, people grant easy-to-process cues higher importance weights (Shah and Oppenheimer 2007, pp. 371–72; see also Oppenheimer 2008). This point has roots in prior work on heuristics, which shows that people more heavily weight easier-to-access cues. For instance, people use brand name perceptions as proxy for product quality (Maheswaran, Mackie, and Chaiken 1992), use ease-ofimageability of attributes (like hallways) as an input for making apartment evaluations (Keller and McGill 1994), and so on. Therefore, if shoppers perceive that calculating the calorie gap is relatively easier, during evaluations they assign more importance weight to the calorie gap. Continuing with the cereal example, if the focal, low-calorie cereal is displayed in an aligned manner, during evaluations shoppers attach relatively higher importance weight to calorie gap information.

This mechanism substantially enhances the generalizability of the subtraction principle mechanism proposed in Biswas et al. (2013). The subtraction principle predicts that displaying the sale price in a nonaligned manner increases subtraction difficulty. In turn, due to subtraction difficulty, shoppers who are less likely to initiate the subtraction task to calculate discount depth assume a 10%–12% discount depth (benchmark from Blair and Landon 1981). This mechanism, especially the point about the assumed discount depth, is fairly specific to the pricing domain. We modify the subtraction principle mechanism and propose that shoppers who are more (less) likely to initiate the subtraction task attach more (less) importance weight to discount depth information (more generally, attribute gap information). Consequent to this modification, the subtraction principle can apply beyond the pricing domain to a wide variety of other product domains.

Furthermore, the studies in Biswas et al. (2013) focus exclusively on the domain of price promotions and imply that shoppers generally prefer an overall lower price, in effect implying that price is an LIB attribute. We point out that there are contexts wherein price may be perceived as an MIB attribute, often for reasons relating to signaling of quality (Dodds, Monroe, and Grewal 1991; Miyazaki, Grewal, and Goodstein 2005; Monroe 1973). In this article, we generalize the work of Biswas et al. (2013), examining both LIB and MIB attributes, while also examining attributes such as calories, which are perceived by some as LIB and perceived by others as MIB. Appendix A lists the aforementioned points and shows the various ways this research modifies and broadens the prior conceptualization of the subtraction principle.

Displaying a focal, healthy food item in an aligned (vs. nonaligned) manner should increase the perceived importance weight of the calorie gap during evaluations. For shoppers with diet goals, the increased importance weight of the calorie gap should increase preference for the focal food item. For shoppers with taste goals, however, the increased importance weight of the gap should enhance their preference for the comparison food item with higher levels of calories and reduce their preference for the focal food item. Formally,

H1: For shoppers with diet (taste) goals, displaying a focal, healthy food item in an aligned manner increases (decreases) choice share of the focal, healthy food item.

H1 is our central hypothesis, stating that presenting food items in an aligned (vs. nonaligned) manner increases goalconsistent food choices and preferences. The next two hypotheses outline the mechanism underlying this central hypothesis. We propose that ( 1) during evaluations, presenting food items in an aligned (vs. nonaligned) manner increases the importance weight placed on the calorie gap (H2), and ( 2) during evaluations, increased importance weight placed on the calorie gap increases the propensity to make goal-consistent food choices (i.e., increases the propensity that shoppers with diet (taste) goals are more (less) likely to choose the focal, healthy food item; H3).

H2: Displaying the focal, healthy food item in an aligned (vs. nonaligned) manner increases the importance weight of attribute gap information during evaluations.

H3: For shoppers with diet (taste) goals, increased importance weight of attribute gap information increases (decreases) choice share of the focal, healthy food item.

Study 1 is a field study in a supermarket and is an initial test of H1. It involves shoppers choosing between regular CocaCola and the newly launched Coca-Cola Life. In Study 2a, we reexamine H1 in a lab study, using a chocolate context, wherein we associate chocolate with either diet goals or taste goals. In Study 2b, we examine the full process model (H1–H3), using a soup can choice context. Given that Studies 1 and 2 relate to the food domain, in Studies 3a and 3b we generalize our findings by examining other product domains. Stimuli exemplars (for all studies) appear in Appendix B.

We ran Study 1 over four days in a supermarket in Stockholm. Coca-Cola (Sweden) provided us with bottles of regular CocaCola (CCR; more calories = 879 kJ3) and of the newly launched soft drink, Coca-Cola Life (CCL; fewer calories = 565 kJ, focal drink). We had access to endcap shelving, which we modified using two different display versions that alternated every few hours, displaying CCL in either an aligned manner (i.e., CCR on the shelf above and CCL on the shelf below) or a nonaligned manner. The shelf-signs clearly showed the kJ values associated with each drink. We specifically clarify that each shopper saw only one of the two display versions.

Shoppers were intercepted and asked to participate in the study. In return, they would receive either CCR or CCL, whichever they preferred. Shoppers examined the display, then chose a CCL or CCR bottle (the experimenters restacked the shelf each time, so shoppers always saw fully stacked CCL and CCR shelving.) Next, shoppers moved to another area, where they completed a short survey. The survey included a shortversion diet intentions scale, with two items from Stice (1998; “I take small helpings in an effort to control my weight,” “I limit the amount of food I eat in an effort to control my weight”; 1–5 scale; 1 = “never,” and 5 = “always”; r = +.59, p < .05), and also included demographics (age and gender). In all, 352 shoppers (Medianage = 20.0 years; 67.9% women) participated in this 2 (display: aligned vs. nonaligned) · continuous (diet intentions scale) between-subjects study.

We used PROCESS (Model 1; Hayes 2013) to investigate the interaction. The dependent variable was soft drink choice (CCR = 0, CCL = 1), and the two independent variables were diet intentions (mean-centered at M = 2.31) and display (nonaligned = –1, aligned = 1). In the logistic regression for soft drink choice, we found significant main effects of diet intentions (b = -.34, SE = .12, z = -2.91, p < .05), no significant main effects of display (z = -.94, p = .35), and (most importantly) a significant interaction effect (b = .32, SE = .12, z = 2.72, p < .05). The positive interaction term indicated that for those with higher diet intention scores, presenting CCR and CCL in an aligned manner increased choice of CCL (the focal, lower-calorie drink).

A floodlight analysis (Table 1) revealed that for those with relatively high diet intention scores (mean-centered scores > 1.68; 7.95% of sample), the simple effect of displaying CCL in an aligned display condition was significantly positive (at score of 1.68: b = .44, SE = .22, z = 1.96, p = .05), implying increased choice share for the lower-calorie CCL. However, for those with low diet intention scores (mean-centered scores < –.43; 30.1% of sample), the simple effect of displaying CCL in an aligned display was significantly negative (at score of -.43: b = -.24, SE = .12, z = –1.96, p = .05), implying decreased choice share for the lower-calorie CCL (and increased choice share for the higher-calorie CCR). Study 1 results are consistent with H1.

In Study 1, shoppers differed in the extent of their diet intentions, reflecting trait dispositions. Moving beyond traits, in Study 2a we acknowledge that shoppers may differ in their (taste vs. diet) goals, contingent on the food item category. That is, the same shopper could have different goals, conditional on differences between food item categories. Some sets of food items (e.g., health foods) may prompt diet goals, but others (e.g., desserts) may be associated with taste goals. To the extent that products prompt different goals, the effects of presenting food items using an aligned (vs. nonaligned) display may differ. To examine this point, in Study 2a we prime participants to associate the same product (in this case, chocolates) with either a taste goal or a diet goal.

Method. This was a 2 (goal association: taste goal vs. diet goal) · 2 (display: aligned vs. nonaligned) between-subjects design, involving 255 undergraduate students (65.1% women) taking a survey in a behavioral lab. Participants were told that the survey was about beliefs and preferences about chocolate. First, we primed chocolate as being associated with either a taste goal or a diet goal, using a mechanism outlined in prior work (e.g., Dhar and Wertenbroch 2000; Roggeveen et al. 2015). For the taste goal, participants were told that “there are many reasons why people eat chocolate. And yet, what is often comes down to, is that people eat chocolate because it makes them happy. At the end of a long day, eating a piece of chocolate is the perfect reward.” Next, participants were asked to write a few words about why people should eat chocolate. Participants generally responded in ways consistent with a taste goal (e.g., “People should eat chocolate because it makes them happy. It feels rewarding to have some at the end of a day,” “It’s a wellearned reward at the end of a long day,” “Eating chocolates make people happy. They think [it’s the] perfect reward”). For the diet goal, participants were told that “there are many reasons why people eat chocolate. Interestingly—and this may not be well known—people should eat chocolate for health reasons. Medical studies have shown that chocolate can not only reduce LDL (bad cholesterol) and increase HDL (good cholesterol) but also reduce the incidence of stroke. Next, participants were asked to write a few words about why people should eat chocolate. Participants generally responded in ways consistent with a diet goal (e.g., “It is good for your cholesterol and can prevent strokes,” “People should eat chocolate because it can improve aspects of your health,” “reduce LDL/increase HDL/reduce chance of a stroke”).

Next, participants were shown two chocolate boxes. Each chocolate in box W had approximately 91 calories, and each chocolate in box K had approximately 68 calories. Participants were shown the two boxes either in an aligned manner or in a nonaligned manner (box W displayed below box K). Finally, participants were asked which box of chocolates they would prefer to take a piece of chocolate from (single-item, 11-point scale; -5 = “box K,” and +5 = “box W”).

Results. We ran an analysis of variance for chocolate preference. We found no significant main effects for display (F( 1, 251) = .02, p > .8), significant main effects for goal association (F( 1, 251) = 18.9, p < .05), and a significant two-way interaction between goal association and display (F( 1, 251) = 10.3, p < .05).

When chocolate was associated with taste goals, participants’ preference for low-calorie chocolate box K was weaker when box K was displayed in an aligned manner (Maligned = .68, SD = 3.54; Mnot aligned = -.69, SD = 3.86; F( 1, 251) = 4.67, p < .05). Put another way, when chocolate was associated with a taste goal, participants’ preference for the higher-calorie chocolate box W was significantly stronger when box W was displayed in an aligned manner. In contrast, when chocolate was associated with a diet goal, participants’ preference for low-calorie chocolate box K was significantly stronger when box K was presented in an aligned manner (Maligned = -2.70, SD = 3.02; Mnot aligned = -1.20, SD = 3.79; F( 1, 251) = 5.64, p < .05). These results are consistent with our central hypothesis, H1.

In Study 2b, we test the full process model across H1–H3. In addition, whereas Studies 1 and 2a involved some version of the attribute “calories,” Study 2b involves the attribute “sodium.”

Method. Two hundred sixty-one U.S. undergraduate students (56.7% women) participated in a two-part study for course credit. First, as part of a set of multiple studies, participants filled out the short, five-point (1 = “never,” and 5 = “always”), sixitem diet intentions scale (Stice 1998; a = .91). The six items were “I take small helpings in an effort to control my weight,” “I limit the amount of food I eat in an effort to control my weight,” “I hold back at meals in an attempt to prevent weight gain,” “I sometimes avoid eating in an attempt to control my weight,” “I skip meals in an effort to control my weight,” and “I sometimes eat only one or two meals a day to try to limit my weight.”

Second, a week later, the same undergraduate students participated in a soup choice study. Because the popular press tends to highlight the negative influences of sodium on health, we did not expect many participants to know that higher sodium (also) can be associated with better taste. Therefore, we asked each participant to read a couple of paragraphs that summarized extracts from various publications, stating that although sodium is associated with obesity and high blood pressure, it also tends to be associated with better taste and aroma. All participants read both paragraphs, such that all participants received twosided information.

Next, participants observed two (similar-looking) cans of chicken soup, next to which we showed the respective sodium levels (can N = 477 mg, can B = 664 mg). Participants also learned that the cans typically contained about two servings each, had 90–100 calories per can, and were similar in their content weight (approximately 19 oz.). These soup cans appeared in either an aligned manner (can N below can B) or a nonaligned manner (can N above can B), leading to a 2 (display: aligned vs. nonaligned) · continuous (diet intentions scale) between-subjects design.

Participants first chose their preferred soup can and indicated the importance weight of various factors for their choice decision by allocating five points across ( 1) sodium content, ( 2) number of servings per can, and ( 3) whether the soup contained chicken. Participants could allocate points however they wished, as long as the total points allocated across the three elements totaled five.

We anticipated that presenting the soup cans in an aligned manner will lead participants to place increased importance weight on sodium content. Among those who scored high on the diet intentions scale, participants who assigned more weight to the sodium content should be more likely to choose lowsodium can N. However, if participants scored low on the diet intentions scale, such that they likely focus more on taste, then those who placed more weight on sodium content should be more likely to choose higher-sodium can B (due to the unhealthy = tasty intuition) and so should be less likely to choose can N and more likely to choose can B.

Results. First, we ran a logistic regression for soup choice (can B = 0, can N = 1), in which the independent variables were the diet intentions score (mean-centered at M = 2.21) and display (not aligned = –1, aligned = 1). The main effect of meancentered diet intentions score was significant (b = .77, SE = .18, z = 4.34, p < .05), the main effect of the vertical display was not significant (z = .30, p = .76) and the interaction effect was significant (b = .69, SE = .18, z = 3.86, p < .05). The positive interaction term indicated that those with higher diet intention scores were more likely to choose the low-sodium soup can in the aligned display condition.

Second, the floodlight analysis (PROCESS Model 1) depicted in Table 2 revealed that for those with mean-centered diet intention scores greater than .36 (34.9% of sample), soup can N was relatively more preferred in the aligned display condition (at score of .36; b = .29, SE = .14, z = 1.96, p = .05). However, for those with (mean-centered) scores less than –.54 (32.2% of sample), the lower-sodium soup can N was relatively less preferred in the aligned display condition (at score of -.54; b = -.33, SE = .17, z = -1.96, p = .05), and higher-sodium soup can B was relatively more preferred. This was consistent with results in prior studies and with H1.

Next, the importance weight that participants assigned to sodium content information was higher in the aligned display condition (Maligned = 3.22, SD = .91; Mnonaligned = 2.02, SD = .72; F( 1, 259) = 140.3, p < .05); this result was consistent with H2. As an important point, prior work (Bialkova, Sasse, and Fenko 2016) has indicated that those with higher

(lower) levels of diet concerns paid more (less) attention to nutrition information, whereas we assert that this is not the case and that those with taste goals (i.e., with lower levels of diet concerns) would continue to pay attention to nutrition information. Consistent with our assertion, there was no correlation between diet intention scores and importance weight for sodium (r = .02, p > .7).

Third, for those with mean-centered diet intentions scores greater than .87 (i.e., with scores 1 SD above the mean diet intentions scale score), the PROCESS (Model 14; Hayes 2013) output indicated that the mediating effect of the importance weight assigned to sodium was significantly positive (95% confidence interval = [.07, .74]). Thus, if participants scored higher on the diet intentions scale (i.e., had diet goals) and placed more importance weight on sodium during the choice process, they were more likely to choose can N. But for participants with mean-centered diet intention scale scores less than –.87 (i.e., with scores 1 SD below the mean diet intentions scale score), the PROCESS (Model 14) output indicated that the mediating effect of the importance weight for sodium was significantly negative (95% confidence interval = [–.57, –.01]). Thus, if participants scored lower on the diet intentions scale (i.e., had taste goals) and placed more importance weight on sodium during the choice process, they were less likely to choose lower-sodium can N and more likely to choose the higher-sodium can B. These results were consistent with H3.

Studies 1 and 2 focused on the food domain. To generalize the results, we examine other product domains. First, in Study 3a, we present an incentive-compatible study involving an LIB scenario that examines choice between two kitchen implements. In Study 3b—in an MIB scenario—we examine a choice between cell phone accessories. In Study 3b, we also examine factors that may moderate the effects in this research.

The hypotheses (H1–H3) are fairly specific to the food domain. However, these hypotheses can easily be modified to extend to any product domain. In Study 3a, for instance, the prediction is that using an aligned display will increase choice share of the focal item.

Method. We administered this two-cell (sale price display: aligned vs. nonaligned) between-subjects design to attendees of three sessions of a cooking class held in a gourmet food store in an upscale U.S. neighborhood. We asked them to participate in a five-minute (voluntary) study, and approximately twothirds of attendees chose to participate. Among these 43 participants, the median age group were those over 50 years old, 79.1% were women, and median household income was $100,000–$200,000.

All participants were given a study booklet. The first page showed two upscale Wusthof brand cooking knives. On the left side of the page, we presented a picture of knife A (comparison knife) along with a brief description and a sale price of $71; on the right side of the page, we presented a picture and a brief description of knife B (focal knife), showing an original price of $114 and a sale price of $77. In the nonaligned display condition, the sale price appeared above the original price, whereas in the aligned display condition, the sale price was shown below. Participants indicated which knife they preferred. On the second page, we provided another set of two Wusthof brand knives (knife C and knife D) using a similar presentation, but here the focal knife was on the left side of the page. The focal knife, knife C, had an original price $102 and a sale price of $75. Knife D (on the right side of the page) served as the comparison knife, with a sale price of $68. Participants again indicated which knife they preferred. Finally, participants provided their gender, age range, and household income range. To ensure that participants took the task seriously, we told participants (prior to starting the study), that they should make careful choices because (in each session) one participant would be randomly selected to receive one of the two knives chosen.

TABLE: TABLE 2 Johnson–Neyman Regions in Study 2b

aMean-centered values for diet intentions scale.

Notes: This table illustrates the conditional effect of alignment on choice of the low-sodium soup can, at values of diet intentions scale. LLCI = lower-limit confidence interval; ULCI = upper-limit confidence interval. Boldfaced cells indicate significance.

Results. Consistent with results in Studies 1 and 2, participants preferred the focal knife relatively more when the sale price was shown in an aligned manner. When sale price was shown below the original price, the choice shares for both focal knives B and C were 84.0%; when sale price appeared above the original price, choice shares for the focal knives fell to 33.3% (knife B) and 61.1% (knife C). These choice share differences were significant for knives A versus B (c2( 1) = 11.49, p < .05) and directionally significant for knives C versus D (c2( 1) = 2.88, p = .09).

This study involved a cell phone battery scenario wherein participants were provided with numeric information on battery life, an MIB attribute. Our incoming expectation was that relative preference for the focal item, with longer battery life, would be greater when it was presented in an aligned (vs. nonaligned) manner. Furthermore, a key element of our theory is that if the focal item and the comparison item are displayed in an aligned manner, then it is relatively easier for the shopper to perform the difference calculation. However, if the difference calculation is very easy in the first place, then alignment differences should not affect the perceived ease of performing difference calculations, thus mitigating our proposed effects.

Methods. Participants from Amazon Mechanical Turk (N = 250, 37.8% women, median age group 26–30 years, median annual income $25–$50,000) took a Qualtrics survey. After an instructional manipulation check question (see Oppenheimer, Meyvis, and Davidenko 2009), the survey outlined a scenario wherein the participants used their cell phone a lot, so participants were aiming to buy a cell phone case with an integrated cell phone battery. Then participants read brief descriptions of two cell phone cases with batteries. Case/ Battery H (the comparison item) had been on the market for six months, earned good reviews, and offered an incremental battery life of 246 minutes. Case/Battery J (the focal item) was a bit thicker, and because it had just launched, reviews were not available, but the manufacturer claimed an incremental battery life of 331 minutes. The participants were randomly assigned to a 2 (display: aligned vs. nonaligned) · 2 (calculations: harder vs. easier) between-subjects design. In the harder difference calculation condition, the battery lives were 246 minutes (H) and 331 minutes (J); in the easy difference calculation condition, battery lives were 250 minutes (H) and 350 minutes (J). We elicited relative preference on a seven-point scale (1 = “strong preference for H,” and 7 = “strong preference for J”) and captured demographic information.

Results. We found that 35.6% participants gave incorrect responses to the instructional manipulation check (consistent with ranges in Oppenheimer et al. [2009]), so we removed them from the analyses (consistent with recommendations in Oppenheimer et al. [2009]). An analysis of variance for relative preference revealed significant main effects (for both display and difference calculations, F( 1, 157) > 3.9, p < .05), as well as a directionally significant interaction effect (F( 1, 157) = 3.31, p = .07). When battery life differences were harder to calculate, using an aligned display (i.e., locating J above H) led to significantly increased preferences for the focal, longer-life battery J (Mabove = 3.98, SD = 1.93 vs. Mbelow = 2.70, SD = 1.59; F( 1, 157) = 9.38, p < .05). In contrast, when difference calculations were easy, locating J above H did not significantly influence relative preferences (Mabove = 4.03, SD = 2.19 vs. Mbelow = 3.85, SD = 1.89; F( 1, 157) = .17, p > .6).

Consistent with the theory in this article and with our a priori expectations, any factor that makes the difference calculation easier should serve as a suitable moderator for our effects. In Study 3b, we examined the specific case of when difference calculations were easy (e.g., 350 - 250), and thus, alignment differences should not influence the importance weight that shoppers would generally give to the difference gap. Other instances when difference calculations should be easier may include when, for example, the difference gap is explicitly stated (e.g., the difference percentage is explicitly shown [“40% more battery life,” “25% less sodium”]) or the difference is very large. In all these cases, there would be no need for the shopper to perform the difference calculations to figure out that the difference gap is substantial, so shoppers should generally give the difference gap relatively high importance weight. These factors—some of which were foreshadowed in Biswas et al. (2013)—all (potentially) constitute moderators to our effects.

For shoppers who have diet goals, presenting a focal, healthy food item in an aligned (vs. nonaligned) manner increases its choice share. In contrast, for shoppers with taste goals, presenting the focal food item in an aligned (vs. nonaligned) manner decreases its choice share and increases the choice share of the competing, less healthy food item. This nonintuitive interaction result reflects our central hypothesis (H1), whereby the extent to which shoppers make made goal-consistent food item choices is higher (lower) when the food items are displayed in an aligned (nonaligned) manner. Drawing on work in food-related research, goals, and numeric cognition, we outline the underlying process mechanism in H2–H3. Whereas Studies 1 and 2 illustrate H1–H3 in the food domain, Study 3 shows that our proposed decisionmaking framework has widespread applicability, potentially extending to any domain wherein advertising, retail displays, or online displays involve comparisons of numeric attribute information.

Our study makes several contributions to the body of work relating to shoppers’ food-related goals and how shoppers make food choices. First, prior research has suggested that people with less focus on diet goals pay less attention to nutrition information (Naylor, Droms, and Haws 2009; Van Herpen and Van Trijp 2011). For example, Mohr, Lichtenstein, and Janiszewski (2012, p. 64) argue that “those very involved with their dietary choices will … [be influenced by] nutrition labels,” and they found (p. 66) that differences in purchase intentions across less healthy versus healthier food items arose among those with higher levels of diet intentions but not among those with lower levels of diet intentions. Bialkova, Sasse, and Fenko (2016) and Cavanagh and Forestell (2013) found similar effects, such that people who were more concerned about health were more likely to prefer healthy food items over less healthy food items, but people less concerned about health expressed no clear preference. In contrast, we propose that people with less focus on diet goals (i.e., those with taste goals) do indeed pay attention to nutrition labels; however, because of the unhealthy = tasty intuition, these shoppers behave as if they prefer food items with more calories or sodium. This explanation better reflects the interaction result in Studies 1–2, implying a specific disordinal (crossover) pattern. If shoppers with taste goals merely ignored (or paid less attention to) nutrition information, the interaction pattern in Studies 1–2 would be different and would reflect an ordinal pattern. In contrast with prior research, we find that it is possible to find cases wherein those score lower on the diet intentions scale behave as if they may prefer higher-calorie food items—especially when attribute information is presented in an aligned manner. This point highlights a key contribution of this article.

Second, prior research into shoppers’ food choices has tended to ignore the impact of display differences (and other contextual differences) related to the presentation of calorie information. Specifically, even as prior research posits that people with diet goals focus more on low-calorie items, it ignores the possibility that these effects may be weaker if lowercalorie food items are displayed in a nonaligned manner. In Studies 1 and 2, the effect of diet intention scores (or diet vs. taste primes in Study 2a) on choices and preferences is moderated by differences in attribute display. In essence, shoppers are more likely to make goal-consistent choices when food items are displayed in an aligned manner. This point is both new and nontrivial. Beyond the implications for practice (as we discuss subsequently), this finding may explain null results that arise when differences in diet intentions do not prompt different choices or preferences. For example, consider Study 2b. We reanalyze the data and, purely for illustrative purposes, median-split the diet intentions variable. If we consider just the two cells reflecting the nonaligned display condition, the relative choice shares for the low-sodium soup can were 39.7% (low diet intentions) versus 43.1% (high diet intentions) (c2( 1) = .15, p = .69). Examining just these cells might lead a researcher to conclude that differences in diet intentions do not affect shoppers’ choices. Yet when we consider the other two cells, which involve aligned food item displays, the relative choice shares shift to 22.2% (low diet intentions) versus 70.7% (high diet intentions) (c2( 1) = 30.65, p < .05). Thus, examining just the data pertaining to an aligned display would lead a researcher to a very different conclusion: that differences in diet intentions significantly affect shoppers’ choices. Both points are contributions beyond modifying and broadening the subtraction principle and are highlighted as such in Appendix A.

The findings in this article also contribute to the numerical cognition literature. By examining the impact of display differences, we determine that vertical display differences lead to varying importance weights that shoppers assign to the attribute gap when making evaluations. Displaying a focal food item in an aligned (vs. nonaligned) manner, such as below (vs. above) comparison food items, leads shoppers to attach more importance weight to the attribute gap in their evaluations. We tested these effects not only within the food domain (Studies 1 and 2) but also in other domains (Study 3), indicating that these effects have predictive applicability across a wide variety of domains wherein advertising, retail displays, and online displays involve comparisons of numeric attribute information.

Given concerns about obesity and associated health problems (Howlett et al. 2012), there is much interest in understanding when shoppers might use calorie and sodium information to make healthier choices. One challenge is to motivate shoppers to embrace diet goals, which can increase their consumption of healthier foods. However, assuming shoppers have diet goals, another challenge is to ensure that available calorie (or sodium content) information is displayed in ways that nudge shoppers toward healthier, rather than less healthy, food choices. Our findings suggest that display differences related to the location of food items can encourage shoppers’ healthy choices. As our studies indicate, diet-focused shoppers are more likely to make healthy choices if nutrition information is displayed in an aligned manner.

Finally, and most importantly, this article outlines a parsimonious decision-making framework that examines how shoppers react to advertising, retail, or online displays in a wide variety of product domains involving comparisons of numeric attribute information. Building from, modifying, and expanding the work in Biswas et al. (2013), we identify two key elements that jointly determine shoppers’ evaluations: ( 1) whether shoppers perceive the attribute as an LIB attribute or a MIB attribute and ( 2) whether the attribute information is displayed in an aligned or nonaligned manner. Firms can use this framework to better design advertising, retail displays, and online displays. The effects in this article apply across a variety of product domains, as evidenced in the marketplace examples we cite and in the range of studies we present (Appendix A).

Differences in shelf displays affect shoppers’ purchase intentions (Grewal et al. 2011). As more firms adopt the voluntary FOP nutrition labeling system, “Facts Up Front,” and as more retailers display food items to showcase such FOP information, a key question is how retailers and category captains should organize the display of food items on retail shelves. The insights in this article offer some guidance. Imagine a retailer that wants to promote the new low-calorie Coca-Cola Life soft drink. If most shoppers (at this retailer) have diet goals, or if the retailer is able to prime diet goals through in-store signage or advertising, then, on the basis of this research, the retailer will increase sales by putting cans of Coca-Cola Life below cans of regular Coca-Cola. If a retailer primarily attracts shoppers with diet goals, but its profit margins are better on regular soups, then it might choose to put the regular soup cans below the lower-sodium soup cans to encourage relatively more sales of regular soup, despite its primarily “diet-goal” shopper segment. Finally, depending on food item categories, advertising, packaging, and so on, shoppers may have different goals. To the extent a retailer knows these goals, or to the extent firms can use advertising or packaging to prime such goals, firms can use display differences to increase sales of the more profitable products within the category. For example, if the candy category prompts taste goals, and if margins are higher on candy products with more calories, then retailers should display higher-calorie candy items above other candy-items to maximize sales of these more profitable products. Thus, the findings in this article can aid retailers and category captains as they optimize in-store shelf displays. Contingent on shoppers’ goals, numeric values relating to calorie content or to sodium, and relative food item profitability, retailers can display food items in ways that “push” certain high-profit food items over others. Similarly, the findings in this article may also inform how online retailers should display food items on their webpages and how supermarkets and grocery items should display food items on flyers.

The effects we describe herein are driven by differences in (locational) displays of food items, which lead to differences in importance weight attached to food item attributes, with downstream consequences. Understanding this informationprocessing sequence has several implications for consumer welfare and public policy. First, we provide guidance for how diet and nutrition apps might be structured to help shoppers make healthy choices, noting that those who use such apps likely already have diet goals. When diet apps provide scores of food items, whether in grocery stores or restaurants, such diet apps should motivate shoppers to not only learn exact calorie/sodium information but also to give this information greater importance weight in their evaluations. Such efforts might help mitigate any negative impact arising from retailers’ use of nonaligned displays. Second, from a public policy perspective, young consumers and children are relatively unlikely to have diet goals (Burton, Wang, and Worsley 2015), and so using aligned food item displays may well backfire. Specifically, using aligned food item displays and/or explicitly prompting younger consumers to consider calorie/sodium information is likely to increase their preference shift toward higher-calorie/sodium food items. In such instances, regulatory policies governing advertising and menu signage should (seemingly counterintuitively) recommend using nonaligned display presentations and should (seemingly counterintuitively, but importantly) avoid prompting younger consumers to explicitly consider calorie/sodium information. These points highlight the role of attribute gap importance weight and distinguish this work from Biswas et al. (2013). Finally, both shoppers and policy makers need to recognize that marketers can present attribute information in ways that may mislead shoppers. For example, if a lower-calorie option involves smaller profit margins, a restaurant frequented by patrons with diet goals might display this option above a high-calorie option to reduce patrons’ weighting of the calorie gap and thus reduce patrons’ preference shift toward the lower-calorie option. Such a practice can be labeled as “providing full information” to patrons and is not illegal, but public policy experts would note that it may reduce welfare.

The effects outlined in this article have widespread applicability, extending well beyond the food domain. As stated previously, these effects apply to any product domain involving a comparison of numeric attributes. Thus, for example, the effects would extend to any product domain wherein prices (typically an LIB domain) are displayed; into any domain involving MIB attributes such as battery life (e.g., cell phones, tablets, laptops) and Internet speed (e.g., cellular networks); and into domains such as robotic vacuum cleaners, which involve attributes that are MIB (e.g., operating time) and LIB (charging time).

As a specific example, some insurance companies (e.g., Progressive) provide information both about their own rates and about the rate from a competitor. Progressive shoppers would behave similarly to shoppers with diet goals, in the sense that insurance shoppers generally prefer lower insurance rates (insurance rates = LIB attribute). In line with this article’s findings, we suggest that an insurance provider should present its own rate quote below the quote from a competitor. If its own rates are lower, this presentation format ensures that insurance shoppers put more weight on the “rate gap,” which increases relative preference for the focal insurance company’s product. If, however, the competitor’s rates are lower, presenting its own (higheramount) rates below would lead shoppers to put less weight on the rate gap, thus decreasing relative preference for the competitor’s insurance product.

The advice in the previous paragraph is valid when the attributes involved are clearly LIB. However, other attributes may typically be perceived as MIB (e.g., network speed, battery life, operating time [in robotic vacuum cleaners]). In such instances, the focal firm should display its information above that of competition (exactly opposite of what is advised when the attribute is LIB). If its competitor’s “scores” are lower, this presentation display ensures that shoppers put more weight on the attribute gap, which increases relative preference for the focal firm’s product. If its competitor’s scores are higher, then this presentation display leads shoppers to put less weight on the attribute gap, thus decreasing relative preference for the competitor’s product.

The contrast across the prior two paragraphs highlights the importance of identifying shoppers’ goals and identifying whether shoppers perceive the attribute as LIB or MIB. As we have noted, what constitutes the optimal display is conditional on how shoppers perceive attributes. It is for this reason that LIB versus MIB is one of the two independent variables in this article.

The effects we find are driven by differences in displays of food items, which lead to varying importance weights assigned to numeric attribute information and further downstream consequences. We reiterate this important point because it helps clarify the conditions in which the effects we propose may be more versus less evident. To the extent that the effects in this article are driven by differences in importance weights, they may be more evident if the importance weight of numeric attribute information (e.g., calories, sodium, price) is neither too high nor too low. However, in certain conditions, such importance might reach high levels, and it is then that the effects we propose may be less evident. For example, if calorie attributes dominate choice (i.e., have very high importance weight), display differences likely have minimal effects, as a result of ceiling effects. Beyond the moderators identified in

Study 3b, these may also constitute moderators of our effects, and these are not predicted by Biswas et al. (2013).

Future research could also further examine the informationprocessing mechanism outlined in H2 and H3. A key element of this mechanism is that subtraction calculations are perceived as more difficult when the smaller number is displayed above the larger number. Numerical cognition researchers may also examine whether individual differences related to numeracy, math anxiety, and so on may moderate the effects noted in H2–H3.

In addition, there are two ways shoppers can perceive numeric attributes (vector attributes vs. ideal point attributes; see Green and Srinivasan 1978; Teas 1993). This article examines the case wherein shoppers perceive attributes as “vector” attributes, preferring either more of an attribute (MIB [e.g., battery life]) or less of an attribute (LIB [e.g., for diet focused shoppers, fewer calories/less sodium), but it does not examine what happens when attributes have ideal point characteristics. What happens if shoppers believe that an “ideal” number of calories for a sandwich is around 350 calories? Future research could explore whether the effects in this article sustain when such ideal points exist for key attributes.

In this research, we assume that consumers who behave as if they prefer food items with more calories do so for taste-related reasons. But other reasons could also be operant, such as financial reasons that prompt some lower-income shoppers to prefer food items with more calories. Examining the behaviors of these shoppers is an important area for research, especially from a policy standpoint, to determine whether lower-income shoppers might prioritize calorie amounts over factors like nutrition or health.

Building on a “healthy-left, unhealthy-right” intuition, Romero and Biswas (2016) propose that a food item without nutrition labeling is perceived as healthier if displayed to the left (vs. right) of a comparison food item. Among shoppers with diet goals, such a display increases the focal, healthy food item’s choice share. However, we propose that when FOP calorie information is shown, displaying the focal, healthy food item to the right of the comparison item (i.e., displaying the food items in an aligned display) may increase the importance weight that shoppers attach to the calorie gap during their evaluations and so would increase the choice share of this food item. Thus, presence of FOP calorie information may reverse Romero and Biswas’s (2016) results. Research that tests these competing predictions could contribute to both theory and practice.

Implicit in our theory is that many shoppers embrace the unhealthy = tasty intuition. It would be worthwhile to reexamine these effects among populations (e.g., in France; see Werle, Trendel, and Ardito 2013) for whom this intuition may be weaker or even reversed. Finally, we only examine cases wherein attribute information is provided using numeric information. However, sometimes attribute information is provided using quasi-numeric formats, such as when Verizon contrasts its cellular coverage with AT&T using a map covered with more (vs. less) dots, without providing information relating to the actual number of dots. Would aligned (vs. nonaligned) display matter in such cases? Examining this and similar questions may further expand the applicability of this work.

1In this section, we contrast a focal food item with fewer calories with a comparison food item with more calories. The theory advanced herein also extends to other comparisons between all types of items described using numerical attribute information.

2Similarly, other, somewhat less well-known research has indicated that people often assume that the presence of increased sodium levels is associated with better taste (e.g., Henney, Taylor, and Boon 2010).

3In Sweden, nutritional values are provided in kilojoules (kJ) and not in calories (1 calorie @ 4.18 kJ).

aNonaligned presentation increases subtraction difficulty (Thomas and Morwitz 2009; see also Fuson and Briars 1990; Yip 2002).

A: Study 1

B: Study 2a

C: Study 2b

D: Study 3a

E: Study 3b

Notes: In Studies 1, 2, and 3a, aligned display conditions are shown. In Study 3b, the aligned display/harder calculation condition is shown.

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appendixes

APPENDIX A

TABLE:

a Nonaligned presentation increases subtraction difficulty (Thomas and Morwitz 2009; see also Fuson and Briars 1990; Yip 2002).

APPENDIX B

Notes: In Studies 1, 2, and 3a, aligned display conditions are shown. In Study 3b, the aligned display/harder calculation condition is shown.

An Integrated Power and Efficiency Model of Contractual Channel Governance: Theory and Empirical Evidence 

Power theories (e.g., social exchange theory, resource dependence theory) and efficiency theories (e.g., transaction cost analysis) offer very different perspectives on the design of contractual governance in marketing channels. Whereas power theory suggests that governance will reflect the preferences of powerful firms, efficiency theories argue that governance will maximize joint value. In this research, the authors provide an integrative framework that reconciles power and efficiency perspectives in the context of contractual marketing channel relationships. This framework discriminates between two methods of exercising power: ex ante (through a tightly specified, efficient contract that rewards the powerful firm through the price mechanism while providing strong safeguards for the weak firm) or ex post (through a loosely specified, inefficient contract that allows the powerful firm to exploit its power during renegotiations). The authors argue that power will cause channel governance to deviate from the efficient choice, but only to the extent that the powerful firm cannot price out (i.e., extract) the value it offers to the weaker firm ex ante. As exchange conditions become more uncertain, power will demonstrate stronger effects on governance. This theory is supported with data from studies on contractual research-and-development relationships and procurement contracts for customized industrial products.

Keywords: contracting; efficiency; power; governance; marketing channels

The design of contractual governance between independent parties in a marketing channel has been informed by two major but strikingly different perspectives: power and efficiency theories. The central claim of power theory is that observed governance will reflect the preferences of powerful firms ([ 2]; [27]; [35]). Efficiency theories, in contrast, argue that governance is chosen to maximize the joint value created in the channel as a whole, irrespective of the initial power differences between the firms ([ 6]; [68]; [73]). Because of this distinction, power and efficiency theories rarely converge on the same governance predictions ([35]; [71]). Indeed, one of the key motives underlying the development of efficiency theories like transaction cost analysis (TCA) was a desire to challenge the standard monopoly (power) argument put forth in applied price-theory approaches to industrial organization and antitrust ([48]). Accordingly, "Greater respect for organizational (as against technological) and for efficiency (as against monopoly) purposes is needed" ([71], p. 17).

The debate between power and efficiency perspectives has been acrimonious at times, with some sociologically inclined power theorists decrying the spread of economic approaches to the study of organization (e.g., [23]). Many institutional economists have been equally derisive of power: "The main problem with power is that the concept is so poorly defined that power can be and is invoked to explain virtually anything" ([71], p. 238). Prominent organizational theorists have similarly described the construct of power as diffuse and disappointing ([51]). Indeed, as early as 1980, Stern and Reve distinguished between these two "seemingly disparate" disciplinary orientations—a primarily economic perspective rooted in industrial organization and focused on efficiency (e.g., [ 4]) versus a primarily behavioral perspective rooted in social psychology and focused on power and conflict (e.g., [24]; [64]). Even then, they expressed a concern about the lack of integration between the two viewpoints: "Rarely have there been attempts to integrate these two perspectives. Indeed, they should be viewed as complementary, because the former deals mainly with economic 'outputs' while the latter is concerned with behavioral 'processes'" ([65], p. 53).[ 5]

Despite the coexistence of these two perspectives in the literature, most empirical work has adhered to a single perspective while doing little more than acknowledging the other (usually in the form of control variables). As such, meaningful integration of the two has been lacking. This indifference to the other perspective has led to certain limitations. For example, power theories have not been able to explain when and why powerful firms might choose governance that enhances joint value versus when they might deviate from this focus to accommodate their own self-interests. Likewise, efficiency theories have been mostly silent on how ex ante firm differences influence governance, although [29], [30]) governance value analysis is an exception. As a result, we have a limited understanding of how the two theories interact, the circumstances under which one or the other will be more determinative of channel governance, and the new insights that can be generated by considering the two within a unified framework.

Constructing one such unifying framework in the context of contractual marketing channel relationships is the goal of this study. Our core thesis is this: power will cause governance to deviate from the efficient structure, but only to the extent that uncertainty prevents the powerful firm from pricing out the value it offers to the weaker firm ex ante. By "pricing out," we mean that the price mechanism, rather than other aspects of the governance structure, is used by the powerful firm to extract the value of the unique resources it brings to the relationship. For example, when exchange conditions are less uncertain, rather than reducing or eliminating the safeguards provided to the weak firm, a powerful firm can instead charge a higher upfront price and couple this with the strong safeguards desired by the weak firm. In this sense, the price mechanism makes it unnecessary for the powerful firm to alter the ex post governance structure in its favor, leaving it in an efficient state. As such, when exchange conditions are less uncertain, power and efficiency theories will arrive at similar governance predictions. In contrast, when exchange conditions are more uncertain, we argue that the powerful firm cannot price out the value it offers and will therefore shift governance to its own advantage and away from the structure predicted by efficiency theories.

To conduct a clear test of this power–efficiency framework, we require settings in which efficiency theories provide an unambiguous prediction that is then compromised by power as exchange conditions become more uncertain. Accordingly, we select two contexts in which the exposed specific investments are predominantly made by the buyer (in the technology/equipment supplied by the vendor), with comparatively limited (nonreimbursed) investments made by the vendor.[ 6] Given this one-sided pattern of exposed investments, the primary exchange hazard is also one-sided: potential opportunism on the part of the vendor against which TCA prescribes safeguards for the buyer.

In both contexts, we focus on the safeguards contained in the ex ante contractual agreement. In the first, where clients sponsor technical development work by external contract research organizations (CROs), we investigate two sets of safeguards: price inflexibility and client-exclusive property rights over the technology. In the second, where industrial buyers purchase customized equipment systems from vendors, we investigate price inflexibility and performance guarantees as safeguards. These contexts enable us to provide evidence on the generalizability of our framework across substantively different governance forms: price inflexibility, property rights, and performance guarantees. Consistent with our thesis, in both contexts we find that when contracting with more powerful vendors, safeguards are weaker only in more uncertain environments. That is, both power and uncertainty must be present to produce an effect on safeguards and pull them away from efficiency-theory predictions.

Our research makes several contributions. First, our integrative framework contributes to both literature streams by describing the conditions under which powerful firms will act to promote efficiency within their channels versus those in which they will bias governance toward their own self-interests and away from efficiency. The current TCA literature is generally silent on when and why firms might choose inefficient governance forms, and the power literature similarly cannot predict when firms will refrain from exercising their power. We also add to the power literature by describing the conditions under which powerful firms will apply their influence ex ante, at the governance selection stage, versus ex post, over activities and bargaining in the ongoing relationship.

Second, our work builds on and complements a small body of research that has worked toward integrating power and efficiency theories, principally [37] and [62]. Both see power as interfering with the standard TCA predictions, but for different reasons. In the context of ongoing supply arrangements, [37] recognize the fundamental conflict between power and efficiency and view the presence of cooperative norms as critical to smoothing concerns about the abuse of ex post power—reverse opportunism in [47] terminology—and allowing the efficient transfer of control as predicted by TCA and property rights theory. Our approach differs in that we focus on ex ante power derived from extrarelational resources and develop the interplay between power and efficiency within the incomplete contracting framework itself, thus allowing us to address governance issues even in relationships not endowed with strong norms ([ 6]; [35]; [59]).

[62] explore the benefits rather than the risks of power and argue that powerful firms are able to use their positions to better coordinate the relationship and lower transaction costs directly, thereby making costly integration less essential. This, in turn, dulls the empirical relationships between the standard TCA predictors and vertical integration. In their approach, power, when present, never fails to weaken the efficiency prediction. That is, there is no factor that inhibits the influence of power on governance and, as a result, their theory cannot predict the conditions under which powerful firms will refrain from influencing governance away from TCA predictions. We suggest a more nuanced interaction in which power moves governance away from efficiency, but only in sufficiently uncertain conditions.

Third, relatedly, our work complements research on the governance value analysis (GVA) framework ([29], [30]) that also uses the incomplete contracting perspective to describe relationships between ex ante firm characteristics and governance forms. Whereas [29], [30]) consider how firms with differentiated (i.e., power-generating) market positions are motivated to choose contracts that protect these extrarelational resources from expropriation ex post, our framework sheds light on the flip side of this issue by considering how these firms can best exploit their power by discriminatingly aligning governance forms with different exchange conditions.

Finally, one of the notable shortcomings of TCA is its incomplete treatment of bounded rationality and differences in firm preferences in the process used to select governance structures. Essentially, it is assumed that firms will somehow automatically "find their way" to the most efficient arrangement. This limitation has gone largely unremarked on since the foundational work of [11]. We contribute by analyzing how different contracting environments change the criterion used to select governance—power or efficiency.

This article is organized as follows. We begin with a brief review of power and efficiency theories. We then develop our unifying framework in the context of contractual channel relationships for two distinct, but conceptually similar, inputs and draw refutable predictions that are tested with primary data. We finally discuss the implications for research and managerial practice.

The power perspective has shaped a significant body of research in marketing, having been used to predict channel performance (e.g., [27]), satisfaction (e.g., [28]), and conflict (e.g., [49]), among other phenomena. Power is most commonly defined as the ability of one party to bring about its desired outcomes by influencing the actions of others ([19]; [39]). Firms are more powerful to the extent they possess unique and difficult-to-imitate resources that make them valuable to potential partners (e.g., [14]). This value gives them a degree of influence over governance selection ex ante as well as activities and bargaining in the ongoing relationship ex post (e.g., [25]; [33]).

The central principle of power theory is that powerful firms will use their positions to align governance structures with their own preferences (e.g., [27]; [35]). In many instances, powerful firms use their influence to organize the channel more efficiently (e.g., [32]; [62]; [65]). In this role, they not only provide information, expertise, and legitimacy to better coordinate channel activities but also resolve sticky channel management problems such as free riding that would otherwise increase transaction costs and reduce efficiency. One illustration of this coordinating role played by powerful firms is the decision control accorded to franchisors by less powerful franchisees ([50]). Franchisors can use this control to enforce territorial restrictions, prevent free riding off the efforts of other franchisees, direct system-wide promotional activities, and take other steps to enhance efficiency. At the same time, power theory recognizes that firms may use their positions in ways that do not enhance joint value, instead favoring themselves at the expense of weaker counterparties (e.g., [33]; [50]). For example, the practice of powerful franchisors forcing franchisor-specific investments on weak franchisees has been frequently cited as an "unfair" abuse of franchisor power ([47]). Similarly, supply relationships with powerful retailers are often heavily biased in the retailer's favor (e.g., [44]).

Despite its intuitive appeal, the power perspective has three shortcomings. First, it is not clear as to when and why powerful firms would choose to play the role of enhancing joint value versus focusing primarily on their own payoff. Second, while power theories have focused on the role of ex ante power, they have failed to distinguish this from ex post power that arises from the "fundamental transformation" in which investments in specific assets lead to a small-numbers bargaining environment ([71]).[ 7] Thus, firms that are powerful ex ante may not always maintain a superior position ex post once they are locked into the relationship ([30]).

The third and most significant shortcoming is that power theories do not use a comparative governance analysis to discriminate between alternative governance forms. A comparative governance analysis considers the choice between alternative governance forms simultaneously, rather than seeking to explain each choice in isolation as power theories do. This limitation is best illustrated by example. Suppose we wish to explain the use of a direct sales force as opposed to independent reps. The power of the firm relative to the salesperson might explain the decision to go direct (i.e., the more powerful firm forces salespeople into employment relationships that are subject to greater control and usually offer lower rewards than those the same individuals could earn as independent contractors). However, under a comparative analysis, it is not clear why vertical integration is the preferred way for the firm to leverage its power over salespeople. Instead, the firm could use its power to bargain for a significantly lower commission rate from independent reps. Power can explain both governance alternatives in isolation but does not discriminate between the two in a comparative fashion.[ 8]

The need for such discrimination is especially critical in contractual channel relationships because they are more nuanced than the rather blunt instrument of vertical integration ([75]). In particular, the more powerful firm could force either an inflexible, tightly specified contract in which it extracts its value through the price mechanism by charging a premium price, or a more flexible, loosely specified contract that allows it to exploit its power and extract value during ex post renegotiations. Both options potentially increase its gains from the relationship; however, it is not clear which governance option the firm would choose and why. [50], p. 22) express this conundrum quite lucidly: "It could certainly be argued that the more powerful channel member would not need an explicit contract because it would not need any safeguards; the weaker party could do little harm to it because of its less powerful position. The problem with this argument is that it goes against the strong empirical evidence that more powerful channel partners, such as franchisors, extensively use explicit contracts."

Efficiency theories such as TCA, property rights theory, and incomplete contracting theory have also driven an enormous body of literature in marketing, having been used to predict vertical integration (e.g., [ 1]), contract structure (e.g., [38]), features of relational exchange (e.g., [36]), partner selection (e.g., [67]), codevelopment alliances (e.g., [22]), and contracts for new product introductions ([15]), among other things. These theories rest on the premise that parties adopt governance structures that maximize joint value ([ 6]; [68]; [71]). Formally, efficiency refers to the minimization of the out-of-pocket and opportunity costs associated with producing and transacting in the channel; hence its equivalence to joint value maximization.[ 9]

This focus on joint value implies that the chosen governance form is invariant to differences in ex ante firm power, because such differences do not affect the joint benefit obtained from adopting efficient governance ([74]). In other words, the fact that one firm has more power ex ante does not alter the optimal governance arrangement within the channel ex post. Rather, ex ante power only affects how the gains from adopting the efficient arrangement are divided among the parties, with more powerful firms extracting a greater share of this "surplus" ([34]; [68]). [11] and [68] illustrate how a system of ex ante transfers, or side payments, can be devised such that each firm will prefer the efficient ex post arrangement after taking the transfers into account. This is because joint profit is maximized under the efficient arrangement; thus, these transfers will always be of sufficient magnitude to compensate the counterparty for its losses in case it is (initially) worse off under the efficient arrangement. The most common forms of ex ante transfers in practice are the price one firm pays to acquire another in the case of integration or the agreed on transfer price (e.g., franchise fee) in a contractual channel setting.

Efficiency theories, however, are subject to important shortcomings of their own that have received relatively little attention. In particular, they are largely silent on the process by which all parties come to accept the efficient governance structure. If efficient governance is Pareto optimal (i.e., makes all firms at least as well off as feasible alternatives), the issue is trivial. However, if efficient governance is not in the best interest of all parties—particularly powerful parties—how is it that they come to accept it? As [68], p. 463) observes, "The process (the game) through which the possibilities for trade are explored is suppressed, the assumption being that it is optimal; or more precisely, that the parties will (somehow) find the optimal process." Thus, even though [71], p. 174) uses natural selection arguments to defend efficiency when he says "the [efficiency] argument relies in a general, background way on the efficacy of competition to perform a sort between more and less efficient modes and to shift resources in favor of the former," he acknowledges the limits of this assumption: "This intuition would nevertheless benefit from a more fully developed theory of the selection process. Transaction cost arguments are thus open to some of the same objections that evolutionary economists have made of orthodoxy."

Crucially, the natural selection arguments invoked in TCA are not simply underdeveloped. They are in fact not well suited to relationships involving powerful firms, because departures from efficiency within the channel need not harm these firms. In particular, a powerful firm could be better off in a less efficient relationship that is biased in its favor, in which case the survival risk from the adoption of inefficient governance falls on the weaker, often replaceable, partner. Natural selection, therefore, cannot be a sufficient argument for efficiency when powerful firms are involved because there is no reason to assume that selection will somehow force a joint profit-maximizing outcome.

Indeed, per the Coase theorem, an efficient solution is only feasible in a frictionless environment characterized by complete contracting where the parties can commit to both the ex ante transfer and to refraining from opportunistic bargaining ex post. In an incomplete contracting environment, there is no assurance that the efficient arrangement can be made acceptable to both parties ([11]; [70]). Said differently, the implementation of an efficient governance structure essentially comes down to whether one party—here, the weaker party—can pay the other party to accept it ([68]). In turn, the powerful firm must be able to credibly commit to the efficient governance structure and assure the weaker party that it will not exercise its power again ex post. If such credible commitments are not feasible, due to incomplete contracting, the efficiency argument against the influence of power breaks down.

The integrated perspective we develop next is generalizable to a wide variety of channel contexts; however, to provide an unambiguous empirical test of the resulting theory, we seek contexts in which the efficient governance structure can be clearly identified so that we can detect departures from efficiency due to power. We do so in two different channels, one in which client firms sponsor technical development work performed by external CROs (Study 1) and a second in which industrial buyers purchase customized equipment systems from vendors (Study 2). These contexts have two key features. First, unlike typical manufacturer–supplier or manufacturer–distributor settings, the nature of the core activity—research and development (R&D) and systems purchasing—is considerably discrete or lumpy. As such, each transaction stands on its own and long-run relational give and take (the "shadow of the future") is not of as much significance. This assures us that contractual terms cannot be trivially renegotiated without some risk of opportunism when ex post adjustments are required. Second, in both settings, the primary exposed specific investments are made by the client/buyer. This implies that the main exchange hazard is opportunism by the CRO/vendor against which the client/buyer requires a safeguard.

Under these two key aspects of our settings, by examining the extent to which CRO/vendor power reduces safeguards for the client/buyer, we can see the effect of power on the efficient governance structure. To add concreteness in the development of the theory, we cast our hypotheses in the context of Study 1. We use the same logic to suggest predictions for Study 2.

In Study 1, client firms sponsor technical development work performed by external CROs. In this setting, safeguards for the client take two primary forms: ( 1) firm fixed prices or prices subject to prespecified adjustment formulas to limit holdup during development and ( 2) property rights provisions assigning exclusive rights over the technology to the client to deter opportunistic leakage following development. We consider the effects of two key independent variables—CRO power, which is measured by the uniqueness of the CRO among potential suppliers at the outset of the relationship, and uncertainty, which refers to the extent of volatility and ambiguity affecting decision making in the exchange ([52]). To arrive at the key hypothesis based on our efficiency × power interaction, we first briefly describe the main effects of these variables as predicted by power and efficiency theories, respectively (recall that each theory is largely silent on the other variable).

Power theory suggests that powerful firms will bargain for their preferred governance forms. In general, powerful firms will want to limit the safeguards provided for the weaker firm because such safeguards are ( 1) costly to provide and ( 2) limit the powerful firm's discretion to exploit its position ex post. In the context of Study 1, powerful CROs will generally prefer more flexible price contracts (e.g., with adjustment formulas negotiated ex post) not only to protect themselves from cost overruns but also to extract value from the client during ex post adjustments prior to transferring the technology. Similarly, they will prefer to secure some usage and ownership rights over the intellectual property developed in the relationship, because this intellectual property often serves as the basis for future development work ([ 7]). Expropriating this value is not feasible when prices are inflexible (i.e., fixed) and the client has exclusive property rights; therefore, powerful CROs have an incentive to bargain against such safeguards in the contract. Thus:

• H1: CRO power is negatively related to the use of contractual clauses that (a) restrict property rights exclusively to the client and (b) specify inflexible prices, ceteris paribus.

Turning to uncertainty, TCA suggests that exchange hazards increase in more uncertain conditions not only because ex post adaptations are larger and more frequent but also because of increased performance ambiguity due to measurement difficulties. Both of these conditions create greater latitude for opportunistic behavior and increase the need to safeguard exposed specific assets from expropriation ([ 1]; [71]). In our context, in which the main specific investments are made by the client, safeguards in the form of less flexible prices and the use of contractual clauses that assign property rights exclusively to the client are both expected to increase as uncertainty increases. Thus:

• H2: Uncertainty is positively related to the use of contractual clauses that (a) restrict property rights exclusively to the client and (b) specify inflexible prices, ceteris paribus.

Next, we turn to the critical interaction between power and efficiency theories. We start from the position that the powerful firm sets the governance structure in the relationship ex ante because it is unique and can bid down potential weak partners until one of them accepts its terms for participation ([50]). In selecting governance, the powerful firm faces two options. First, it could opt for efficient governance in which joint value is maximized. In our context, such an efficient governance structure would feature a tightly specified contract that provides strong safeguards for the client's investments while rewarding the powerful CRO by enabling it to extract (i.e., price out) the unique value it provides through the price mechanism ex ante. Alternately, the powerful firm may select an inefficient governance form that gives it an opportunity to maximize its own direct rewards from the relationship.[10] In our context, this governance structure would be more informal, with weaker safeguards for the client, thereby allowing the powerful CRO to exploit its power during ex post renegotiations.

Which option will a powerful CRO choose? Note that the powerful firm must be at least as well off under the inefficient as opposed to the efficient governance structure; otherwise it would not choose the former. Because, by definition, joint value must be lower for the inefficient structure, the weak firm is necessarily worse off under the inefficient arrangement.[11] The weak firm will therefore prefer the efficient arrangement and has an incentive to offer an ex ante transfer in exchange for a commitment from the powerful firm to the efficient governance structure. In turn, the powerful firm will accept this only if it is better off following the ex ante transfer; otherwise it will prefer the inefficient arrangement.

When conditions (i.e., low uncertainty) permit more complete contracts to be constructed, the first-best strategy for a powerful CRO will be to choose the efficient governance structure in exchange for an ex ante transfer of sufficient magnitude to make it better off. In such circumstances, both firms will prefer efficient governance, and ex ante power differences between the two firms will not cause a deviation from this choice because the powerful firm has extracted its value ex ante.

In contrast, when complete contracts are more difficult to construct (i.e., high uncertainty), the picture is quite different. First, note that the weak firm will be reluctant to offer an ex ante transfer without a reciprocal commitment guaranteeing the powerful firm's actions ex post; otherwise it could be held up repeatedly. Recourse could be made at this point to the self-enforcing aspects of informal contracts based on reputation ([47]). However, given that the more powerful firm will typically have better prospects in the event of termination, informal contracts are not necessarily self-enforcing in situations where power is highly imbalanced. Importantly, this inability to rely on the self-enforcing mechanism actually works against the powerful firm by making it harder to commit to the efficient arrangement.

Given this inability to commit, a powerful CRO will seek a second-best method to capture the value it offers to the client. It does so by implementing an inefficient governance structure with weak (or nonexistent) safeguards that maximizes its ability to extend its ex ante power into the relationship and expropriate value through ex post bargaining. Importantly, the powerful CRO is actually worse off in this case than in the first-best scenario (i.e., complete contracting where its power is priced out ex ante) because, given the inability to commit, the client's incentives to invest under an inefficient governance structure are lower. However, given the constraints on contracting, this second-best strategy is optimal from the powerful CRO's perspective.

Note the distinction between our thesis and the classic power-oriented view. The latter suggests that the weak firm should accept the terms offered by the powerful firm, even if they are one-sided, because they either reduce uncertainty for the weaker party ([21]) or set parameters on what the more powerful party can do ([50]). In contrast, our argument recognizes not only the role of the price mechanism and its interplay with the choice of ex post governance but also the proactive role played by the nominally weaker party. In less uncertain conditions, the weak firm offers an appropriate transfer payment ex ante with the expectation that the powerful firm will credibly commit to limiting its influence ex post. The better the commitment offered (i.e., the closer safeguards are to the efficient level), the higher the initial transfer payment the weak firm is willing to make. In contrast, in more uncertain conditions, the powerful firm is unable to make such a commitment. As such, the option of pricing out power through an ex ante payment becomes untenable, making the powerful firm choose an inefficient governance arrangement to extract the value from its unique resources ex post. This enables us to tackle the [50] conundrum by explaining why powerful firms use tightly constructed explicit contracts in some situations and not in others. Thus:

• H3: The negative effect of CRO power on the use of contractual clauses that (a) restrict property rights exclusively to the client and (b) specify inflexible prices becomes stronger as environmental uncertainty increases, ceteris paribus.

Outsourced R&D, in which client companies sponsor R&D work by external CROs, is quite prevalent in high-technology, research intensive industries. In these settings, the client makes specific investments in a technology developed by the CRO, which is reimbursed for its client-specific development work. These relationships are governed by a development agreement, which is a formal contract specifying the nature of the deliverables, pricing terms, and property rights provisions for the technology and its derivatives ([ 7]). Given the one-sided nature of specific investments, the efficient governance structure will tend to feature strong safeguards for the client, which consist of less flexible pricing terms and client-exclusive property rights, as described previously.

The context of sponsored R&D projects is not well suited to the use of archival data for several reasons. First, very few data sets are available on outsourced R&D projects in general, and most secondary archives (e.g., the Deloitte/ReCap database) cover only large R&D alliances in narrowly defined industries. Second, our unit of analysis is an individual outsourced R&D project; thus, firm-level archival data would be only indirectly related to any given project and might introduce aggregation bias into the analysis. Third, certain constructs (e.g., the number of qualified contractors available at the outset of the project, financial investments in the project, uncertainty) are difficult to measure without relying on key informants. For example, uncertainty could be measured by volatility in industry or company sales, but these would be at best crude and indirect measures of the uncertainty surrounding an individual R&D project. Thus, we used our own survey instrument to capture the nuances of the context.

Many of the constructs we measure have meanings that are well-known and widely shared among informants in the industry, such as firm fixed prices ([61]). We therefore use single-item, grounded measures (i.e., with defined metrics) to capture the constructs of interest wherever possible. As [61] and [16] observe, trying to introduce additional items into the measurement of such constructs can cause measures to drift from the well-defined meanings. We use psychometric measures only where a domain sampling approach is supportive of validity. In particular, the measure of uncertainty is best captured by a multi-item reflective scale due to the multifaceted nature of this domain, particularly as it relates to contractual incompleteness.

Given our focus on high-technology, research intensive industries where outsourced R&D is common, we used National Science Foundation data to select the top five three-digit industries in terms of the percentage of firms outsourcing R&D: drugs and medicines; optical, surgical, and photographic instruments; communications equipment; motor vehicles and equipment; and aircraft and missiles. We used a national list broker to compile a list of 2,600 R&D managers with a single manager per firm. After eliminating 635 bad contacts, we contacted each manager to solicit participation. We used a snowballing technique to identify a qualified informant, even if different from the original contact. A total of 670 initial contacts could not be reached after extensive effort. Among the 1,295 individuals successfully contacted by telephone, 226 reported that no R&D was conducted in their unit and could not connect us with another manager in their company involved in R&D. A substantially larger number of 496 individuals were involved in R&D but indicated that their company did not outsource these activities. Again, this number includes only those instances in which a referral to a manager in a unit that did outsource R&D was not possible. Another 168 managers were involved with outsourced R&D projects but refused to participate. This left 405 qualified informants who verbally agreed to participate in the mail questionnaire. Follow-up phone calls and a second mailing resulted in 124 useable surveys with complete data on the variables included in this study. This yielded a 30.6% response rate based on the number of surveys mailed (124 of 405) or a 21.6% response rate based on the number of qualified informants identified (124 of 573). The former proportion is similar to the approximately 30% response rate in [36] and 37% in [30], though neither study distinguishes between the number of surveys mailed and number of qualified informants.[12] One drawback of operating in the context of outsourced R&D is that firms are sensitive to disclosing their R&D partners because this is often perceived as a source of competitive advantage. Thus, we were only able to collect data from the client side of each dyad.

We provide the measurement items following purification and response scales in Web Appendix W1. The following subsections describe our measures.

Price inflexibility takes on a value of one if the contract features a firm fixed price or a fixed price with a prespecified adjustment formula and zero if negotiated adjustments or unspecified pricing arrangements such cost-plus are used instead ([10]).

Property rights include ownership rights, usage rights, and rights to future improvements and derivatives ([ 7]). Note that these categories are not mutually exclusive; for example, a client may own the patents to a new technology, giving it residual rights of control associated with ownership ([34]), but certain explicit rights of usage may be granted to the CRO, or the client's usage may be restricted to particular fields of use. Thus, ownership does not preclude the importance of measuring usage rights or rights to derivatives. We employ a three-part measure of the exclusivity of client property rights over the technology developed under the contract consisting of ( 1) exclusive client ownership of patents over, or rights to patent, the technology; ( 2) exclusive client usage rights over the technology; and ( 3) exclusive client rights to derivatives based on the technology. A score of zero or one is assigned for each item and the scores are totaled. This measure is treated as an ordered categorical variable in the estimation.

Our measure of power is based on monopoly theory, in which power is a function of the availability of substitute providers. This measure has been previously used by [31]. Specifically, we measure the number of equally or better qualified CROs available to the client at the formation of the contract. We then reverse code this measure to create the score for CRO power so that a higher score indicates greater power. This measure is based on the perceptions of the client, which is appropriate as they also determine the influence the CRO has in the upfront bargaining.

Uncertainty is driven by a number of factors that combine to determine the overall challenge of operating in a given environment. Uncertainty is classically operationalized in terms of volatility and ambiguity (e.g., [52]). Volatility arises when conditions change frequently in ways that are difficult to predict ex ante ([41]), yet are readily understood ex post ([57]). Ambiguity, in contrast, arises when conditions are difficult to interpret ex post, irrespective of the rate of change ([52]). Because various aspects of volatility and ambiguity can lead to incomplete contracting conditions, we define the construct broadly in this study following a domain sampling approach. The four subdomains are market volatility, defined as the frequency of unanticipated changes in the market relevant to the technology ([36]; [41]; [66]); technological volatility, defined as the frequency of unanticipated changes in the technologies used in the development process ([36]; [40]; [66]); ambiguity, defined as the extent to which signals are open to multiple interpretations ([13]; [52]); and the level of creativity required to perform the development work (another aspect of ambiguity as described by [ 5]]), defined as the extent to which the intent of the project was to develop a meaningfully novel technology ([ 3]. Items for the market volatility scale were adapted from [56], [40], and [36]; items for the technological volatility scale were adapted from [36] and [40]; items for the ambiguity dimension are taken from [ 8] based on [13]; and the required creativity scale is based on [ 3].

Critically, relationships affected by both volatility and ambiguity pose the greatest challenge for decision making ([ 9]). Therefore, we formulate a measure that increases as each aspect of uncertainty increases to capture the overall effect of the various dimensions. This approach offers the key benefit of smoothing the measure across the individual facets. To see the importance of this, consider a simple example. A relationship with low volatility may not be characterized by low uncertainty if ambiguity is high. We use a second-order factor model to provide support for the multidimensional construction of uncertainty and subject it to a confirmatory factor analysis following [43]. Fit statistics following purification suggest an inability to reject the proposed model (χ²(86) = 101.938, p =.116; goodness-of-fit index =.909; root mean square error of approximation =.037; Tucker–Lewis index =.970). The composite reliability of the measure is.925 ([69]). We do not include other indicators in the factor analysis because they are not reflective measures. We averaged the items within each subdomain and then equally weighted the subdomains to form the final score. The control variables used in this study are as follows:

We assess client investment with a grounded measure of the client's total dollar investment in the work performed by the CRO. We used seven categories to measure expenditures: Less than $100,000, $100,000–$249,999, $250,000–$499,999, $500,000–$999,999, $1,000,000–$2,499,000, $2,500,000–$4,999,999, and $5,000,000 or more. Likewise, CRO investment is captured with a parallel measure of its nonreimbursed dollar investment in the project. The response categories for CRO investment are identical to those for client investment, except that we break the less than $100,000 category into two categories because CRO investments are often quite low: $0–$9,999 and $10,000–$99,999.

Repeated exchange can also support complex governance arrangements, making contractual safeguards less necessary. In development relationships, we often have continuity when the CRO is also the supplier of components (or products) based on the newly developed technology. In such cases, we expect more flexible prices and less exclusive client property rights. A single grounded item was used to measure whether the CRO was involved in a subsequent supply relationship with the client.

Contract research organizations often hold patents over technologies that are used to produce the deliverables. When such patents are involved in the development work, the CRO is both more powerful and far more likely to hold property rights to protect its interests. Thus, we expect more flexible prices and less exclusive client property rights. The use of the CRO's preexisting patents in the development work was measured using a single grounded measure.

Even uniquely qualified CROs at the start of the relationship will hold less power if the client also executes a portion of the work internally ([17]). This reduces the CRO's ability to distort governance away from efficiency, resulting in more extensive safeguards for the client. We measured the portion of the work on the technology performed in-house in four ways: as a percentage of dollar expenditures, value added, hours, and number of employees.

In large R&D projects, clients might employ multiple CROs to perform different portions of the overall work on a given technology (there are no cases of CROs performing the same work or "R&D contests" in our data). Because these CROs do different work, the involvement of multiple CROs does not necessarily make them more replaceable. However, the division of the work makes each CRO less important in a general sense, and thus less powerful, giving clients greater safeguards. We assessed this construct using a single grounded measure.

Finally, the nature of the outsourced task was assessed with a grounded measure taking on a value of one if the task consisted of development work and zero if the task consisted of either basic or applied research. The location of the work was similarly captured by a control variable taking on a value of one if the CRO's work was performed at the client's location.

Client exclusivity of property rights and price inflexibility were estimated using an ordered probit and a standard probit model, respectively.[13] The equations are:

$\begin{array}{l}{\text{Client-Exclusive Property Rights}}_{\text{i}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1\mathrm{C}\mathrm{R}\mathrm{O}\mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}{\mathrm{r}}_{\mathrm{i}}\\ +{\mathrm{\beta }}_2\mathrm{U}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{t}{\mathrm{y}}_{\mathrm{i}}\\ +{\mathrm{\beta }}_3\mathrm{C}\mathrm{R}\mathrm{O}\mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}{\mathrm{r}}_{\mathrm{i}}\times \mathrm{U}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{t}{\mathrm{y}}_{\mathrm{i}}\\ +{\mathrm{\beta }}_4\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}{\mathrm{t}}_{\mathrm{i}}+{\mathrm{\beta }}_5\mathrm{C}\mathrm{R}\mathrm{O}\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}{\mathrm{t}}_{\mathrm{i}}\\ +{\mathrm{\beta }}_6\mathrm{C}\mathrm{o}\mathrm{m}\mathrm{p}\mathrm{o}\mathrm{n}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{S}\mathrm{u}\mathrm{p}\mathrm{p}\mathrm{l}{\mathrm{y}}_{\mathrm{i}}+{\mathrm{\beta }}_7\mathrm{C}\mathrm{R}\mathrm{O }\mathrm{P}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}{\mathrm{s}}_{\mathrm{i}}\\ +{\mathrm{\beta }}_8\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{P}\mathrm{e}\mathrm{r}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{D}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{a}\mathrm{r}{\mathrm{s}}_{\mathrm{i}}\\ +{\mathrm{\beta }}_9\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{P}\mathrm{e}\mathrm{r}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}\mathrm{A}\mathrm{d}\mathrm{d}\mathrm{e}{\mathrm{d}}_{\mathrm{i}}\\ +{\mathrm{\beta }}_{10}\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{P}\mathrm{e}\mathrm{r}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t }\mathrm{H}\mathrm{o}\mathrm{u}\mathrm{r}{\mathrm{s}}_{\mathrm{i}}\\ +{\mathrm{\beta }}_{11}\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{P}\mathrm{e}\mathrm{r}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{E}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{o}\mathrm{y}\mathrm{e}\mathrm{e}{\mathrm{s}}_{\mathrm{i}}+{\mathrm{\beta }}_{12}\mathrm{E}\mathrm{x}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{C}\mathrm{R}\mathrm{O}{\mathrm{s}}_{\mathrm{i}}\\ +{\mathrm{\beta }}_{13}\mathrm{D}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{l}\mathrm{o}\mathrm{p}\mathrm{m}\mathrm{e}\mathrm{n}{\mathrm{t}}_{\mathrm{i}}+{\mathrm{\beta }}_{14}\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t }\mathrm{L}\mathrm{o}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}{\mathrm{n}}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}},\text{ and}\end{array}$